CHAPTER II

LITERATURE REVIEW

2.1 Introduction

Authentication is the process in which the system identifies legitimate users from

unauthorized users. Apart from traditional authentication methods biometric systems have been

actively emerging in various industries for the past few years, and it is continuing to advance to

provide higher security features for access control system. Until now there are numerous types of

single modal biometric systems have been developed and organized, which includes fingerprint,

face, speaker, palmprint and hand geometry verification systems. Later to improve higher

security feature, multimodal biometric systems have been developed, which ensures the

characteristics of multimodal biometric as reliable and able to provide high security features.

Some work in multimodal biometric identification systems have been reported in this chapter of

literature.

2.2 Hand Geometry and Palmprint Biometrics

The field of personal verification using palmprint features has drawn considerable

attention and researchers have proposed various methods. The palmprint features are said to be

composed of principal lines, wrinkles, minutiae, delta points of the palm. The authentication

research also focuses on hand geometry which is one of the many effective biometric systems.

Hand geometry refers to the geometric structure of the hand that is composed of the lengths of

fingers, the widths of fingers, and the width of a palm. Later on both palmprint and hand

geometry features have been combined to create an effective authentication system. The

advantages of palm print and hand geometry system are that it is a moderately simple method

that can use low resolution images and provides high efficiency. Some of the related works have

been discussed below in table 2.1.

Table 2.1: Hand Geometry and Palmprint Biometrics

Author(s) Purpose(s) Description(s)

Raul Sanchez

Reillo et.al (2000)

Feature

extraction and

Classification

Used Neural Networks for classification

and Euclidean distance.

Ajay Kumar et.al

(2003)

Feature

extraction

Investigated decision level fusion

scheme, with max rule on palmprint and

hand geometry.

Michael Goh Kah

Ong et.al (2003)

Classification Fused Support Vector Machine with

Radial Basis Function (RBF) kernel on

palmprint and hand geometry verification

system.

Nongluk

Covavisaruch et.al

(2005)

Feature

extraction

Compared six different distance

functions.

Vandana Roy et.al

(2006)

Feature selection Proposed a feature selection technique to

enhance the performance of hand-

geometry.

Peter VARCHOL

et.al (2007)

Feature

extraction

Presented biometric security system for

access control based on hand geometry

Mario Karlovčec

(2010)

Classification Applied K-Nearest-Neighbor and

distance functions.

B.Mathivanan et.al

(2012)

Feature

extraction

Combined of multiple biometric features

extracted from 3-D and 2-D images of the

human hand.

Caroline et.al

(2014)

Analysis Analyzed hand geometry for personal

identification.

Raul Sanchez Reillo et.al [90] adopted Neural Networks for classification with the help of

Euclidean distance and 97% of success in classification was achieved.

Ajay Kumar et.al [1] investigated decision level fusion scheme, with max rule on palmprint and

hand geometry features, and to achieve higher performance that may not be possible with single

biometric indicator alone. The results obtained from 100 users, demonstrated that this was indeed

the case.

Michael Goh Kah Ong et.al [64] fused SVM with RBF kernel has been compare with on

palmprint and hand geometry and two combined classifiers, namely non-weighted sum rule and

weighted sum rule. The SVM with RBF kernel had shown the highest rate of 99.99% .

Nongluk Covavisaruch et.al [76] used six different distance functions such as, Absolute

Distance, Weighted Absolute Distance, Euclidean Distance, Weighted Euclidean Distance, D1

Distance, S1 Distance were tested and compared. Test data are from 96 users. Among the six

different distance functions, S1 gives the best results in both verification and identification.

Vandana Roy et.al [114] proposed a feature selection technique to enhance the performance of

hand-geometry based authentication system. With the transformed features, the False Accept

Rate (FAR) and False Reject Rate (FRR) of the system were reported to be 8.3% and 0.8%

respectively over a test set of 120 samples and it was experimentally shown that the performance

of the authentication greatly improved.

Peter VARCHOL et.al [79] presented biometric security system for access control based on hand

geometry. Experiments show that the physical dimensions of a human hand contain information

that is capable to verify the identity of an individual. The database created for our system

consists of 408 hand images. Different pattern recognition techniques have been tested to be

used for verification and achieved experimental results FAR=0.1812% and FRR=14.583%

showed the possibilities of using this system in environment with medium security level with full

acceptance from all users.

Mario Karlovcec [57] proposed to perform identification and verification based on machine

learning methods, in particular K-Nearest-Neighbor and distance functions. Experimental results

on verification showed that The K-Nearest-Neighbor method outperforms the distance functions.

B.Mathivanan et.al [59] presented a new approach to achieve reliable personal authentication

based on simultaneous extraction and combination of multiple biometric features extracted from

3-D and 2-D images of the human hand. Experimental results on a database of 150 images taken

from 50 subjects demonstrate that the combining 2-D and 3-D hand-geometry features have high

discriminatory information for biometric verification.

Caroline et.al [16] analyzed the geometric features of hand such as length and width of hand and

fingers in providing personal authentication, which can be stepped into financial sectors for

access entry applications.

2.2 Knuckle Biometrics

Finger knuckle region is the outer surface around the phalangeal joint of human finger, which is

found to have inbuilt patterns of highly unique and can serve as a distinct biometric identifier.

Abundant line-like textures are contained in the outer finger surface has the potential to do

personal authentication. Various research works have been done on this area and few works are

enlisted below in table 2.2.

Table 2.2: Knuckle Biometrics

Author(s) Purpose(s) Description(s)

Ch. Ravikanth et.al

(2007)

Developing a

new system

Investigated a new biometric system

based on texture of the hand knuckles.

Ajay Kumar et.al

(2009)

Component

analysis

Developed a system using orientation

knuckle features and texture

Ajay Kumar et.al

(2009)

Contactless

imaging

Employed on palm dorsal hand vein

images using weighted score level

combination.

Lin Zhang et.al

(2009)

Feature

Extraction

Considered for personal authentication

technique using finger-knuckle-print

(FKP) imaging and the local convex

direction map.

Marina Comparison Powered by novel fusion methods, such

GAVRILOVA

et.al (2010)

as Markov Chain model, fuzzy logic and

chaotic neural networks, in multi-modal

systems.

Michael K.O. Goh

et.al (2010)

Feature

extraction and

Classification.

Proposed a novel palm print and knuckle

print tracking approach to automatically

detect and capture these features from

low resolution video stream.

Wankou YANG

et.al (2011)

Feature

extraction and

Classification.

Used Gabor feature and a multi-

manifold discriminant analysis

(MMDA) method to identify finger-

knuckle-print

Kelani Nitesh et.al

(2012)

Feature

extraction

Introduced 2D finger-knuckle-print

(FKP) for personal authentication using

the local convex direction map.

B. Mathivanan

et.al (2012)

Recognition Addressed a hybrid model for biometrics

based human recognition system.

Mounir Amraou

et.al (2012)

Classification. Presented a novel approach for finger-

knuckle-print recognition combining

classifiers based on both micro texture

in spatial domain provided by local

binary pattern (LBP) and macro

information in frequency domain

acquired from the discrete cosine

transform (DCT) to represent FKP

image.

Shruti Shah et.al

(2012)

Feature

extraction and

Classification.

Dealt on finger knuckle extraction and

recognition of the knuckle surface by

using artificial neural network.

Ch. Ravikanth et.al [91] investigated a new biometric system based on texture of the hand

knuckles and makes the surface a distinctive biometric identifier. Hand geometry features can be

acquired from the same image, at the same time and integrated to improve the performance of

the system. The finger back surface images from each of the users are used to extract scale,

translation and rotational invariant knuckle images. The proposed system, especially on the peg-

free and noncontact imaging setup, achieved promising results on database of 105 users.

Ajay Kumar et.al [2] developed a system for extracts the knuckle texture and simultaneously

acquires finger geometry features to reliably authenticate the users. The proposed method of

knuckle region segmentation, finger ring detection, and the extraction of finger geometry

features has been quite effective in achieving higher performance. Database from 105 users and

achieved promising results.

Ajay Kumar et.al [3] automated and employed on palm dorsal hand vein images acquired from

the low-cost, near infrared, contactless imaging. The knuckle tips were used as key points for the

image normalization and extraction of region of interest. The matching scores are generated in

two parallel stages, hierarchical matching score from the four topologies of triangulation in the

binarized vein structures and from the geometrical features consisting of knuckle point perimeter

distances in the acquired images. The weighted score level combination from these two matching

scores are used to authenticate the individuals. The achieved experimental results from the

proposed system using contactless palm dorsal-hand vein images were promising (equal error

rate of 1.14%).

Lin Zhang et.al [52] considered the personal authentication technique using finger-knuckle-print

(FKP) imaging and the local convex direction map of the FKP image was then extracted, based

on which a coordinate system is defined to align the images and a region of interest (ROI) is

cropped for feature extraction and matching. To match two FKPs, Band-Limited Phase-Only

Correlation (BLPOC) based method to register the images and further to evaluate their similarity.

An FKP database is established to examine the performance of the proposed method, and the

promising experimental results demonstrated its advantage over the existing finger-back surface

based biometric systems.

Michael K.O. Goh et.al [65] proposed an innovative contact-less palm print and knuckle print

recognition system and a novel palm print and knuckle print tracking approach to automatically

detect and capture these features from low resolution video stream. The palm print and knuckle

print features were extracted using the proposed Wavelet Gabor Competitive Code and Ridget

Transform methods. Several decision-level fusion rules are used to consolidate the scores output

by the palm print and knuckle print experts. The fusion of these features yields promising result

of EER=1.25% for verification rate.

Wankou YANG et.al [119] used Gabor feature and a multi-manifold discriminant analysis

(MMDA) method to identify finger-knuckle-print and Local binary pattern (LBP) for texture

classification. The experimental results showed that proposed method could work well.

Kelani Nitesh et.al [47] introduced to use 2D finger-knuckle-print (FKP) as a new biometric

identifier for personal authentication. The local convex direction map of the FKP image was

extracted, based on which a coordinate system is defined to align the images and a region of

interest (ROI) is cropped for feature extraction. For efficient FKP matching, a feature extraction

scheme is introduced to exploit both orientation and magnitude information extracted by Gabor

filters. This technique has a great potential to be future improved and employed in real

commercial applications.

B. Mathivanan et.al [60] addressed a hybrid model for biometrics based human recognition

system using the dorsum of hand and the finger knuckle print. Dorsum of hand (backside of hand

or topside of hand) is the opposite side of the palm side of the hand. Both the finger knuckle print

and hand shape features were proposed to be extracted from the single hand image acquired from

a top mounted camera setup and some unique features that improve the accuracy of the

recognition. Several more significant hand attributes that can be used to represent hand shape

and improve the performance are examined.

Mounir Amraou et.al [69] presented a novel approach for finger-knuckle-print recognition

combining classifiers based on both micro texture in spatial domain provided by local binary

pattern (LBP) and macro information in frequency domain acquired from the discrete cosine

transform (DCT) to represent FKP image. The classification of these two feature sets is

performed by using support vector machines (SVMs), which had been shown to be superior to

traditional pattern classifiers. The experiments clearly show the superiority of the proposed

classifier combination approaches over individual classifiers on the recently published PolyU

knuckle database.

Shruti Shah et.al [101] dealt on finger knuckle extraction and recognition of the knuckle surface

by using artificial neural network. The finger knuckle prints were extracted using edge detection

applied for dimensionality reduction. Artificial neural network played a major role for

classifying the knuckle surface and it is achieved by using distance measure. Here the result

highlighted that the proposed method was more feasible and effective.

2.4. Speech Biometrics

Speech recognition is a process used to recognize speech uttered by a speaker and has

been in the field of research for more than five decades. Speech recognition is an important and

emerging technology with great potential. The significance of speech recognition lies in its

simplicity. This simplicity together with the ease of operating a device using speech has lots of

advantages and can be used in authentication. Until now, the extensive literature of speech

recognition is reviewed in table 2.3

Table 2.3: Speech Biometrics

Author(s) Purpose(s) Description(s)

Trent W. et.al

(2003)

Automatic

speech

recognition

Performed Automatic speech recognition

(ASR) for Audio-Visual Speech

Recognition(AVSR)

Giuseppe Richardi

et.al (2005)

Active learning Solved the problem of adaptive learning,

in automatic speech recognition.

Patricia Melin et.al

(2006)

Analyzing

unknown speaker

Described neural networks, fuzzy logic

and genetic algorithms for voice

recognition by analyzing the sound

signals.

Mohamed

Afifyet.al (2007)

Clustering Developed variational Bayesian (VB)

estimation and clustering techniques.

Mustapha

Guezouri et.al

(2009)

Vowel

classification

Aimed to neural network as speech

recognition based on temporal radial

basis function “TRBF”.

K.Sreenivasa Rao

et.al (2011)

Analyzing

prosodic

Explored neural networks to model the

prosodic parameters of the syllables from

parameters their positional, contextual and

phonological features.

Geoffrey Hinton

et.al (2012)

Applied DNN Used feed-forward neural network over

Deep neural networks (DNNs).

Dong Yu et.al

(2012)

Employed MLP Employed deep neural networks (DNNs)

with MLPs (multi-layer perceptrons).

Pethalakshmi et.al

(2012)

Applied

fMAPLR

Constructed a speech biometric system

by using fMAPLR algorithm.

Md. Ali Hossain

et.al (2013)

Feature

extraction and

Classification.

Concerned with Back-propagation

Neural Network for Bangla Speech

Recognition by Mel Frequency Cepstral

Coefficient (MFCC) analysis.

Kunjithapatham

Meena et.al

(2013)

Gender

classification

Used speech processing for classification

of gender and proposed a new method

using used fuzzy logic and neural

network.

Trent W. et.al [113] performed Automatic speech recognition (ASR) for Audio-Visual Speech

Recognition(AVSR) and examined how useful this extraction technique in combination with

several integration architectures and compares it with competing techniques, demonstrated that

vision does in fact assist speech recognition when used in a linguistically guided fashion, and

gives insight into remaining issues.

Giuseppe Richardi et.al [34] developed the technique to solve the problem of adaptive learning,

in automatic speech recognition and also proposed an active learning algorithm for ASR.

Patricia Melin et.al [77] described neural networks, fuzzy logic and genetic algorithms for voice

recognition by analyzing the sound signals. Neural networks used for analyzing the sound signal

of an unknown speaker, and after this first step, a set of type-2 fuzzy rules was used for decision

making, fuzzy logic used due to the uncertainty of the decision process and Genetic algorithms to

optimize the architecture of the neural networks.

Mustapha Guezouri et.al [70] interested in spatio-temporal neural network as speech recognition

and developed based on temporal radial basis function “TRBF” looking to many advantages: few

parameters, speed convergence and time invariance. This application aimed to identify vowels

taken from natural speech samples from the Timit corpus of American speech. The researcher

reports a recognition accuracy of 98.06% in training and 90.13 in test on a subset of 6 vowel

phonemes.

K.Sreenivasa Rao et.al [105] explored neural networks to model the prosodic parameters of the

syllables from their positional, contextual and phonological features. The prosodic parameters

considered in this work are duration and sequence of pitch values of the syllables. These prosody

models are further examined for applications such as text to speech synthesis, speech

recognition, and speaker recognition and language identification. Neural network models in

voice conversion system were explored for capturing the mapping functions between source and

target speakers at source, system and prosodic levels and used neural network models for

characterizing the emotions present in speech. For identification of dialects in Hindi, neural

network models were used to capture the dialect specific information from spectral and prosodic

features of speech.

Geoffrey Hinton et.al [33] used feed-forward neural network over HMM states as output. Deep

neural networks (DNNs) that have many hidden layers and are trained using new methods had

been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by

a large margin and represented successes in using DNNs for acoustic modeling in speech

recognition.

Dong Yu et.al [25] employed deep neural networks (DNNs) to improve detection accuracy over

conventional shallow MLPs (multi-layer perceptrons) with one hidden layer. A range of DNN

architectures with five to seven hidden layers and up to 2048 hidden units per layer have been

explored and the proposed DNNs achieved significant improvements over the shallow MLPs,

producing greater than 90% frame-level attribute estimation accuracies for all 21 attributes tested

for the full system. Telephony speech known as on the phone detection task obtained excellent

frame-level accuracy of 86.6%.

Pethalakshmi et.al [80] constructed a speech biometric person authentication system, in which

fMAPLR algorithm is employed. It proposed a flexible tying scheme that allows the bias vectors

and the matrices to be associated with different regression classes, such that both parameters are

given sufficient statistics in a speaker verification task.

Md. Ali Hossain et.al [62] concerned with Back-propagation Neural Network for Bangla Speech

Recognition. Ten bangla digits were recorded from ten speakers and have been recognized. The

features of these speech digits were extracted by the method of Mel Frequency Cepstral

Coefficient (MFCC) analysis. The MFCC features of five speakers were used to train the

network with Back propagation algorithm. The MFCC features of ten bangla digit speeches,

from 0 to 9, of another five speakers were used to test the system. The developed system

achieved recognition rate about 96.332% for known speakers (i.e., speaker dependent) and 92%

for unknown speakers (i.e., speaker independent).

Kunjithapatham Meena et.al [49] used speech processing for classification of gender and

proposed a new method using used fuzzy logic and neural network. To train fuzzy logic and

neural network, training dataset is generated and mean value is calculated for the obtained result

from fuzzy logic and neural network. By using the threshold value, the proposed method

identified the speaker belongs to which gender. The implementation result shows the

performance of the proposed technique in gender classification.

2.5 Multimodal Biometrics

A multimodal biometric system employs information from multiple cues to authenticate

a user. When a multimodal biometric system integrates two or more biometric identifiers, it

becomes capable of individual biometric to provide greater performance and higher reliability.

Multimodal biometric system overcomes the problems like noisy sensor data, non-universality,

intra-class variations, spoof attacks and unacceptable error rates which unimodal biometric

systems may suffer. Multimodal biometric systems are having multiple independent biometrics

which brings out the higher reliability and increased performance and efficiency. Multimodal

biometric systems are the main focus of the recent research era and some of the works on

multimodal biometric system have been reported below in table 2.4.

Table 2.4: Multimodal Biometrics

Author(s) Purpose(s) Description(s)

Kyong Chang et.al (2002) New multimodal

biometrics

Compared the performance

of unimodal biometrics and

multimodal biometrics

M. Indovina et. al (2003) Performance analysis Examined the performance

of multimodal biometric

authentication systems using

state-of-the-art Commercial

Off-the-Shelf (COTS)

fingerprint and face

biometrics

Arun Ross et. al (2004) New approach in

biometrics

Discussed the multimodal

biometric systems.

K.Maghooli et. al (2004) Creating new

multimodal biometric

system

Proposed a new approach in

multimodal biometric

authentication based on

AdaBoost

Julian Fierrez-Aguilar et.

al (2005)

Fusion strategy for

quality

Presented a novel score-level

fusion strategy based on

quality measures for

multimodal biometric

authentication.

Robert Snelick et. al

(2005)

Performance evaluation

of multimodal biometric

system

Examined the performance

of multimodal biometric

authentication systems using

state-of-the-art Commercial

Off-the-Shelf (COTS)

fingerprint and face

biometric systems

Sheng Zhang et. al (2005) Integrating multimodal

biometrics

Described a system by

integrating multimodal

passive biometrics in a

Bayesian framework.

Rahal, S.M. et. al (2006) New combination of Deployed fingerprint and

biometrics face recognition can form a

good combination for a

multimodal biometric

system.

Nikola Pavešić et. al

(2006)

New biometric system Presented a multimodal

biometric authentication

system based on features of

the human hand.

Vivek K. Aggithaya et. al

(2008)

Improve the accuracy in

authentication

Presented a new personal

authentication system that

simultaneously exploits 2D

and 3D palmprint features

aim to improve the accuracy

C. Nandini et. al (2009) Introducing new

biometric system

Introduced a multimodal

biometric system, which

integrates face, ear and

fingerprint recognition

Nageshkumar.M et. al

(2009)

New biometric system

using two traits

Proposed an authentication

method for a multimodal

biometric system

identification using two traits

i.e. face and palmprint.

Mohamed Soltane et. al

(2010)

New fusion strategy Proposed the use of finite

Gaussian mixture modal

(GMM) based Expectation

Maximization (EM)

estimated algorithm for score

level data fusion.

K.Sasidhar et. al (2010) Performance analysis of

multimodal biometric

system

Proved that multimodal

biometric systems had

provided better performance

and accuracy.

P.Tamil Selvi et. al

(2010)

Efficient biometric

system

Proposed an efficient

approach based on

multimodal biometrics (Iris

and fingerprint) based user

authentication

Sorin Soviany et. al Applying multiple Presented a multimodal

(2011) classifiers approach for biometric

authentication, based on

multiple classifiers

A. Jameer Basha et. al

(2011)

Overcome the

limitations of same

identity

Implemented Multimodal

biometric systems to

overcome the limitations by

using multiple pieces of

evidence of the same identity

A. Merati et. al (2012) Analysing the

performance

Investigated the merit of

di erent combinations of the

aforementioned information

sources in uni-modal and

multi-modal biometric

systems

Punam Bedi et. al (2012) Enhance the accuracy

by watermarking

Applied to secure and

authenticate the biometric

data, enhance accuracy of

recognition and reduce

bandwidth and presented a

robust multimodal biometric

image watermarking scheme

using Particle Swarm

Optimization (PSO).

Shari Trewin et. al (2012) Analyzing the biometric

and password

authentication

Examined three biometric

authentication modalities

such as voice, face and

gesture, as well as password

entry.

Moganeshwaran, R. et. al

(2012)

Multimodal biometric

authentication in FPGA

Discussed the System-on-

Chip (SOC) Field

Programmable Gate Array

(FPGA) based

implementation of

multimodal biometric

authentication.

Soyuj Kumar Sahoo et. al

(2012)

Analysis on

classification of

biometric systems

Provided a review of

multimodal biometric person

authentication systems with a

discussion on the

classification of biometric

systems

R.Manju et. al (2012) Effective fusion scheme Presented an effective fusion

scheme that combines face,

iris, and fingerprint.

A. Muthukumar et. al Increased number of Combined more number of

(2012) modalities modalities for biometric

authentication system, based

on an evolutionary

algorithm, Particle Swarm

Optimization.

R.Vinothkanna et. al

(2012)

Match score level

fusion

Compared the biometric

authentication system and

used fusing of both

fingerprint matching score

and gait signal matching

score.

Youssef Elmir et. al

(2012)

Feature extraction and

fusion

Carried out to identification

and authentication based on

the fusion of modalities

using Gabor filters for

feature extraction and

Nalwa's method.

Pethalakshmi et.al (2012) Enhancing the security

and privacy of

multimodal biometric

system.

Applied Least Mean Square

Filtering algorithm for

improving the security and

privacy of multimodal

biometric system.

Caroline et.al (2012) Analyzing various ways

for recognition.

Reviewed multimodal

biometrics, its applications,

challenges and areas of

research

Mohamed Soltane et. al

(2013)

Different modalities for

authentication

Concentrated on fingerprints,

hand geometry, face, voice,

lip movement, gait, and iris

patterns for authentication.

Dapinder kaur et. al

(2013)

Analyzing different

biometric trait

Combined the different

biometric traits and provides

better recognition

performance

Divyakant T Meva et. al

(2013)

Different fusion

techniques

Adopted different fusion

techniques in Multimodal

biometric authentication to

resolve number of issues

presented in unimodal

biometrics

S.Sudarvizhi et. al (2013) Continuous biometric

system

Attempted to build a

continuous biometric

authentication system and

proposed method with the

most suitable modalities.

Varsha S.Upare et. al

(2013)

Multimodal biometric

system for high security

Provided a Multimodal

biometric technology for

continuous user-to-device

authentication in high

security to protect high

security mobile ad hoc

networks (MANETs).

Intrusion detection systems

(IDSs).

Shruthi.B.M et. al (2013) Easy to implement

biometric system

Employed a new approach to

improve the authentication

by using finger vein and low

resolution fingerprint

images.

Aranuwa, F.O. et. al

(2013)

Fusion technique Considered a classical

classifier fusion technique,

Dempster’s rule of

combination proposed in

Dempster-Shafer Theory

(DST) of evidence.

A. Annis Fathima et. al

(2013)

New person

authentication system

Aimed at developing a multi-

modal, multi-sensor based

Person Authentication

System (PAS) using JDL

model.

Sumeet Kaur et. al (2013) Integration of two or

more biometrics

Introduced the multimodal

biometrics that integrates

two or more biometric

identifiers, with the

capabilities of each biometric

to provide greater

performance and higher

reliability.

Bairagi Nath Behera et. al

(2013)

Watermarking

technique

Presented multi-modal

biometric watermarking

techniques for personal

identification system based

on DCT and Phase

Congruency model.

Eshwarappa M.N. et. al

(2013)

New multimodal

biometric system

Developed a multimodal

biometric system using

speech, signature and

handwriting information

Caroline et.al (2013) Analyzing the various

methods in multimodal

biometrics

Analyzed the integration of

multimodal biometrics and

its research area.

Pethalakshmi et.al [2013] New multimodal

biometric system

Developed a multimodal

biometric system by using

2D,3D hand geometry,

2D,3D palmprint, knuckle

feature and speech and

applied various fusion

techniques.

Pethalakshmi et.al Improving the security Encryption of the biometric

data is carried out using RSA

algorithm.

Pethalakshmi et.al Preserving the privacy Piecewise polynomial

function is applied to

preserve the privacy of

biometric data.

Pethalakshmi et.al Analyzing the Provided a review of

multimodal biometric

system

multimodal biometric person

authentication systems with a

discussion on modalities and

security in biometric systems

Kyong Chang et.al [50] compared the results of ear and face biometric recognition as unimodal

biometrics and formed a multimodal biometric recognition system by combining ear and face

biometrics. The results showed that multimodal recognition proved statistically significant

improvement over the unimodal biometric.

M. Indovina et. al [38] examined the performance of multimodal biometric authentication

systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometrics

on a population approaching 1000 individuals. Prior studies of multimodal biometrics had been

limited to relatively low accuracy non-COTS systems and populations approximately 10 % of

this size. Our work is the first to demonstrate that multimodal fingerprint and face biometric

systems can achieve significant accuracy gains over either biometric alone, even when using

already highly accurate COTS systems on a relatively large-scale population and novel methods

of fusion and normalization that improve accuracy still further through population analysis.

Arun Ross et. al [8] discussed the various scenarios that were possible in multimodal biometric

systems, the levels of fusion that are plausible and the integration strategies that can be adopted

to consolidate information and presented several examples of multimodal systems that have

been described in the literature.

K.Maghooli et. al [55] proposed a new approach in multimodal biometric authentication based

on AdaBoost and also compared the results with three different unimodal systems. From the

results, it was explicitly shown that multimodal biometric authentication was performed well.

Julian Fierrez-Aguilar et. al [42] presented a novel score-level fusion strategy based on quality

measures for multimodal biometric authentication. In the proposed method, the fusion function

was adapted every time an authentication claim is performed based on the estimated quality of

the sensed biometric signals at this time. Experimental results combining written signatures and

quality-labelled fingerprints were reported. The proposed scheme was shown to outperform

significantly the fusion approach without considering quality signals. In particular, a relative

improvement of approximately 20% is obtained on the publicly available MCYT bimodal

database.

Robert Snelick et. al [93] examined the performance of multimodal biometric authentication

systems using state-of-the-art Commercial Off-the-Shelf (COTS) fingerprint and face biometric

systems on a population approaching 1,000 individuals. Majority of prior studies of multimodal

biometrics have been limited to relatively low accuracy non-COTS systems and populations of a

few hundred users and demonstrated that multimodal fingerprint and face biometric systems can

achieve significant accuracy gains over either biometric alone, even when using highly accurate

COTS systems on a relatively large-scale population. In addition to examining well-known

multimodal methods, the new methods of normalization and fusion that further improved the

accuracy.

Sheng Zhang et. al [100] described a system that continually verifies the presence/participation

of a logged-in user by integrating multimodal passive biometrics in a Bayesian framework that

combines both temporal and modality information holistically, rather than sequentially. This

allows the system to output the probability that the user is still present even when there is no

observation. Implementation of the continuous verification system is distributed and extensible,

so it is easy to plug in additional asynchronous modalities, even when they are remotely

generated, based on real data resulting from our implementation, the results to be promising.

Rahal, S.M. et. al [88] deployed fingerprint and face recognition can form a good combination

for a multimodal biometric system and where the system design in its hardware and software

parts was done. The hardware part involves the capture devices, fingerprint signal processing

unit, & PC. The software part includes the system software, databases, and face recognition

module. The implementation of the system prototype as "access control system" with the suitable

features was done.

Nikola Pavešić et. al [74] presented a multimodal biometric authentication system based on

features of the human hand. A new biometric approach to biometric authentication based on

eigen-coefficients of palm, fingers between first and third phalanx, and finger tips, is described.

The system was tested on a database containing 10 grey-level images of the left hand and 10

grey-level images of the right hand of 43 people. Preliminary experimental results showed high

accuracy of the system in terms of the correct recognition rate (99.49 %) and the equal error rate

(0.025 %).

Vivek K. Aggithaya et. al [118] presented a new personal authentication system that

simultaneously exploits 2D and 3D palmprint features to aim to improve the accuracy and

robustness of existing palmprint authentication systems using 3D palmprint features. The

proposed system uses an active stereo technique, structured light, to capture 3D image or range

data of the palm and a registered intensity image simultaneously. The surface curvature based

method is employed to extract features from 3D palmprint and Gabor feature based competitive

coding scheme is used for 2D representation. We individually analyze these representations and

attempt to combine them with score level fusion technique. Our experiments on a database of

108 subjects achieve significant improvement in performance (Equal Error Rate) with the

integration of 3D features as compared to the case when 2D palmprint features alone are

employed.

C. Nandini et. al [73] introduced a multimodal biometric system, which integrates face, ear and

fingerprint recognition in making a personal identification. This system takes advantage of the

capabilities of each individual biometrics. Preliminary experimental results demonstrate that the

identity established by multimodal systems is more reliable than the identity established by a

face recognition system alone. In addition, the proposed decision fusion enables the performance

improvement by integrating multiple ones with different confidence measures. Further,

multibiometric systems provide anti-spoofing measures by making it difficult for an intruder to

spoof multiple biometric traits simultaneously.

Nageshkumar.M et. al [72] proposed an authentication method for a multimodal biometric

system identification using two traits i.e. face and palmprint. The proposed system was designed

for application where the training data contains a face and palmprint. Integrating the palmprint

and face features increases robustness of the person authentication. The final decision is made by

fusion at matching score level architecture in which features vectors are created independently

for query measures and are then compared to the enrolment template, which are stored during

database preparation. Multimodal biometric system is developed through fusion of face and

palmprint recognition.

Mohamed Soltane et. al [67] proposed the use of finite Gaussian mixture modal (GMM) based

Expectation Maximization (EM) estimated algorithm for score level data fusion. These biometric

systems for personal authentication and identification are based upon physiological or behavioral

features which are typically distinctive, Multi-biometric systems, which consolidate information

from multiple biometric sources, are gaining popularity because they are able to overcome

limitations such as non-universality, noisy sensor data, large intra-user variations and

susceptibility to spoof attacks that are commonly encountered in mono modal biometric systems.

K.Sasidhar et. al [97] proved that Unimodal biometric systems had many disadvantages

regarding performance and accuracy. Multimodal biometric systems perform better than

unimodal biometric systems and are popular even more complex also and examined the accuracy

and performance of multimodal biometric authentication systems using state of the art

Commercial Off- The-Shelf (COTS) products. Here the researcher discusses fingerprint and face

biometric systems, decision and fusion techniques used in these systems.

P.Tamil Selvi et. al [112] proposed an efficient approach based on multimodal biometrics (Iris

and fingerprint) based user authentication and key exchange system. In this system, image

processing techniques were used to extract a biometric measurement from the fingerprint and iris

directly applied to strengthen existing password or biometric based systems without requiring

additional computation.

Sorin Soviany et. al [103] presented a multimodal approach for biometric authentication, based

on multiple classifiers and used a post-classification biometric fusion method in which the

biometric data classifiers outputs are combined in order to improve the overall biometric system

performance by decreasing the classification error rates. This work showed also the biometric

recognition task improvement by means of a carefully feature selection, as much as not the entire

feature vectors components support the accuracy improvement.

A. Jameer Basha et. al [41] implemented Multimodal biometric systems to overcome the

limitations by using multiple pieces of evidence of the same identity. However, the multimodal

biometric system is limited to the time constraints due to its multiple processing stages. To

improve the speed of authentication in the biometric system with acceptable accuracy, the

researchers have introduced a dynamic fingerprint verification technique fused with enhanced

iris recognition using the adaptive rank level fusion method. When tested upon the standard

biometric dataset the system shows improvement in the False Acceptance Rate (FAR) and Equal

Error Rate (EER) curves. Essentially, the time taken for the training and verification phase has a

reduction of 10% when compared with the existing systems. The multimodel system has

necessarily increased the speed and performance of the verification system especially when

tested on slow processing and low memory devices.

A. Merati et. al [63] investigated the merit of di erent combinations of the aforementioned

information sources in uni-modal and multi-modal biometric systems and showed the

performance of a combination of any two information sources is better than that of using one of

them alone. This work proposed two frameworks for combining information sources in multi-

modal fusion: (1) Joint Fusion (2) Naive Bayesian Fusion. The Naive Bayesian fusion is derived

using the assumption of independence between expert outputs as well as information sources and

also showed that the Naive Bayesian fusion outperforms the Joint fusion in all combinations. The

di erence between these two strategies becomes more significant when the number of experts

involved in the fusion increases.

Punam Bedi et. al [87] applied to secure and authenticate the biometric data, enhance accuracy

of recognition and reduce bandwidth and presented a robust multimodal biometric image

watermarking scheme using Particle Swarm Optimization (PSO). The key idea was to watermark

an individual's face image with his fingerprint image and demographic data. PSO was used to

select best DCT coefficients in the face image for embedding the watermark. The objective

function for PSO is based on the human visual perception capability and ability of the

watermarked image to sustain image processing attacks. Experimental results showed that the

proposed technique embeds private biometric data securely in another biometric content without

affecting the latter's visual quality. The embedding technique ensures, that at the receiver's side

when the watermark is extracted, the practical usefulness of both the biometrics remains intact

leading to a more secure and reliable system of personal recognition.

Shari Trewin et. al [99] examined three biometric authentication modalities – voice, face and

gesture, as well as password entry, on a mobile device, to explore the relative demands on user

time, effort, error and task disruption and provided observations of user actions, strategies, and

reactions to the authentication methods. Face and voice biometrics conditions were faster than

password entry. Speaking a PIN was the fastest for biometric sample entry, but short-term

memory recall was better in the face verification condition. None of the authentication conditions

were considered very usable. In conditions that combined two biometric entry methods, the time

to acquire the biometric samples was shorter than if acquired separately but they were very

unpopular and had high memory task error rates. These quantitative results demonstrated

cognitive and motor differences between biometric authentication modalities, and inform policy

decisions in selecting authentication methods.

Moganeshwaran, R. et. al [66] discussed the System-on-Chip (SOC) Field Programmable Gate

Array (FPGA) based implementation of multimodal biometric authentication. By adjusting

certain part of each biometric system modal, the complex processing unit can be eliminated

allowing for embedded system implementation. As the first step, fingerprint and fingervein were

used as biometric traits and the whole process is implemented in SOC FPGA and executed by

general purpose embedded processor in which the biometric information fusion strategy applies

at the matching score level. The accuracy of the system is promising with an Error Equal Rate

(EER) of 0.33%.

Soyuj Kumar Sahoo et. al [104] provided a review of multimodal biometric person

authentication systems with a discussion on the classification of biometric systems, their

strengths, and limitations. Detailed descriptions on the multimodal biometric person

authentication system, different modes of operation, and integration scenarios are also provided.

Considering the importance of information fusion in multi-biometric approach, a separate section

is dedicated on the different levels of fusion, which include sensor-level, feature-level, score-

level, rank-level, and abstract-level fusions, and also different rules of fusion.

R.Manju et. al [56] presented an effective fusion scheme that combines information presented

by multiple domain experts based on the rank-level fusion integration method. The developed

multimodal biometric system possesses a number of unique qualities, starting from utilizing

principal component analysis and Fisher’s linear Discriminant methods for individual matchers

(face, iris, and fingerprint) identity authentication and utilizing the novel rank-level fusion

method in order to consolidate the results obtained from different biometric matchers. The results

indicate that fusion of individual modalities can improve the overall performance of the

biometric system, even in the presence of low quality data.

A. Muthukumar et. al [71] analysed that the identification and verification are done by

passwords, PIN number, etc., which is easily cracked by others. In order to avoid these attacks,

the multimodal biometrics by combining of more modalities are adapted. In a biometric

authentication system, the acceptance or rejection of an entity is dependent on the similarity

score falling above or below the threshold and focused on the security of the biometric system,

because compromised biometric templates cannot be revoked or reissued and also this research

paper has proposed a multimodal system based on an evolutionary algorithm, Particle Swarm

Optimization that adapts for varying security environments. With these two concerns, this

research paper had developed a design incorporating adaptability, authenticity and security.

R.Vinothkanna et. al [117] used multimodal biometric systems are those which utilize, or

capability of utilizing, more than one physiological or behavioral characteristic for enrollment,

verification or identification. Multimodal biometric authentication system provides some of the

major advantages like accuracy, anti-spoofing etc. Multimodal biometric systems consolidate the

evidence presented by multiple biometric sources and typically provide better recognition

performance compared to systems based on a single biometric modality. Fingerprint

authentication is one of the widely used biometrics because of its universality, easy to use, and

accuracy. When compared to remaining biometric authentication system, gait signal

authentication system is not frequently used because of its complexity in nature. So in this

research work the researchers used fusing of both fingerprint matching score and gait signal

matching score to improve the accuracy of our multimodal biometric authentication system.

Youssef Elmir et. al [121] carried out to enhance the performance of identification and

authentication based on the fusion of those modalities. In multimodal biometric systems, the

most common fusion approach is integration at the matching score level, but it is necessary to

compare this strategy of fusion to the other strategies, like fusion at feature level. This system

combines these two biometric traits and provides better recognition performance compared with

single biometric systems. Multimodal Authentication Systems: The first monomodal verification

system is based on face verification using Gabor filters for feature extraction. The second system

is based on online signature verification using Nalwa's method. The classification is released

using the Cosine Mahalano-bis distance. Due to its efficiency, the researchers have used max-of-

scores strategy to fuse face and online signature scores. The second proposed system is based on

fusion at the feature level.

Pethalakshmi et.al [82] applied Least Mean Square algorithm, one of the adaptive filtering

technique to the multimodal biometric system to enhance the security and privacy of the system.

The results show the improved accuracy level.

Caroline et.al [14] reviewed the multimodal biometrics, which includes its applications,

challenges and areas of research in multimodal biometrics.

Dapinder kaur et. al [19] combined the different biometric traits and provides better recognition

performance as compared to the systems based on single biometric trait or modality and

discussed the various techniques used in level fusion with the objective of improving

performance and robustness.

Mohamed Soltane et. al [68] concentrate on fingerprints, hand geometry, face, voice, lip

movement, gait, and iris patterns for authentication. Multi-biometric systems, which consolidate

information from multiple biometric sources, are gaining popularity because they are able to

overcome limitations such as non-universality, noisy sensor data, large intra-user variations and

susceptibility to spoof attacks that are commonly encountered in uni-biometric systems.

Divyakant T Meva et. al [23] adopted Multimodal biometric authentication to resolve number

of issues presented in unimodal biometrics. There are number of ways for the fusion of different

modalities in multimodal biometrics. Fusion could be either before matching the scores or after

matching the score and dealt with the comparative study of different techniques which performs

fusion of information after matching.

S.Sudarvizhi et. al [107] attempted to provide a comprehensive survey of research on the

underlying building blocks required to build a continuous biometric authentication system. The

first challenge is the choice of biometric. The proposed method revealed that sclera, mouse

dynamics are the most suitable modalities. The challenge of unavailability of observation of one

or more modalities at a particular time is addressed in the section on fusion of modalities. This

has to overcome by the proposed method to invent new methods to reduce the error rates and to

improve the accuracy and speed of the systems.

Varsha S.Upare et. al [115] provided a potential solutions using Multimodal biometric

technology for continuous user-to-device authentication in high security mobile Adhoc networks.

Continuous user authentication is an important prevention-based approach to protect high

security mobile ad hoc networks (MANETs). Intrusion detection systems (IDSs) were also

important in MANETs to effectively identify malicious activities and presented a framework of

combining authentication and intrusion detection in MANET. This research work dealt three

authentication methods to choose the optimal scheme of combining authentication and intrusion

detection. The first method uses the dynamic programming-based hidden Markov model

scheduling algorithms to derive the optimal schemes. The second technique uses the Dumpster–

Shafer theory for data fusion. The system decides whether user authentication (or IDS input) is

required and which biosensors (or IDSs) should be chosen, depending on the security posture.

Third technique presents structural results method to solve the problem for a large network with

a variety of nodes.

Shruthi.B.M et. al [102] employed a new approach to improve the authentication. The system

simultaneously acquires the finger vein and low resolution fingerprint images and combines

these two evidences using a two new score level combination strategy i.e., holistic and nonlinear

fusion.

Aranuwa, F.O. et. al [6] considered a classical classifier fusion technique, Dempster’s rule of

combination proposed in Dempster-Shafer Theory (DST) of evidence. DST provides useful

computational scheme for integrating accumulative evidences and possesses the potential to

update the prior every time a new data is added in the database. However, it has some

shortcomings. Dempster Shafer evidence combination has this inability to respond adequately to

the fusion of different basic belief assignments (bbas) of evidences, even when the level of

conflict between sources is low. It also has this tendency of completely ignoring plausibility in

the measure of its belief. To solve these problems, this research paper presents a modified

Dempster’s rule of combination for multimodal biometric authentication which integrates

hyperbolic tangent (tanh) estimators to overcome the inadequate normalization steps done in the

original Dempster’s rule of combination also adopted a multi-level decision threshold to its

measure of belief to model the modified Dempster Shafer rule of combination.

A. Annis Fathima et. al [5] aimed at developing a multi-modal, multi-sensor based Person

Authentication System (PAS) using JDL model. This research investigated the necessity of

multiple sensors, multiple recognition algorithms and multiple levels of fusion and their

efficiency for a Person Authentication System (PAS) with face, fingerprint and iris biometrics.

Multiple modalities address the issues of non-universality encountered by unimodal systems. The

PAS can be aptly addressed as ‘smart’ since several environmental factors have been considered

in the design. If one sensor is not functional, others contribute to the system making it fault-

tolerant. The final aggregation concluded whether ‘The Person is Authenticated or not’.

Sumeet Kaur et. al [108] introduced the concepts of multimodal biometrics that integrates two

or more biometric identifiers and takes advantage of the capabilities of each biometric to provide

greater performance and higher reliability. It also discussed various levels of fusion, the

importance of fusion at the feature level and a comparative study using different algorithms for

performance analysis.

Bairagi Nath Behera et. al [10] presented multi-modal biometric watermarking techniques for

personal identification system based on DCT and Phase Congruency model. The proposal made

here is an improved algorithm for embedding biometrics data (such as fingerprint image with

demographic information of person) in the face image of the same individual for authentication

and recognition which can be employed in E-passport and E-identification cards. Phase

congruency model is used to compute embedding locations having the low frequency on DCT

coefficients of face image and Normalization correlation based on both human perceptivity and

robust property is used for embedding watermark in these locations. This enhances Quality,

Recognition accuracy and Robustness of both cover and watermark image with minimum

computational complexity. Experimental results demonstrate that the proposed watermark

technique is better robust or resilient against different type of image processing attacks.

Eshwarappa M.N. et. al [30] developed a multimodal biometric system using speech, signature

and handwriting information and demonstrated that the fusion of multiple biometrics helps to

minimize the system error rates. As a result, the identification performance was 100% and

verification performances, False Acceptance Rate (FAR) are 0%, and False Rejection Rate

(FRR) is 0%.

Caroline et.al [15] analyzed the integration multiple sources in multimodal biometric system,

multi algorithm approach, applications and its research areas. The different fusion techniques of

multimodal biometric system are also reviewed.

Pethalakshmi et.al [81] introduced a new multimodal biometric system and studied the

performance of different fusion techniques and fusion rules in the context of a multimodal

biometric system based on the finger print, hand geometry, knuckle extraction and speech traits

of a user. Experiments showed that these fusion techniques showed a marked performance serial

rule showed comparatively better performance.

Pethalakshmi et.al [83] used RSA algorithm for data encryption. The encrypted data are fused by

using various fusion techniques to form the multimodal biometric system. The results of fusion

were studied for both data without encryption and with encryption.

Pethalakshmi et.al [84] proposes a new Piecewise polynomial filtering function for enhancing

privacy-preserving of the data. This function introduces a basis of the corresponding linear space

and then applies the linear combinations of these basis functions.

Pethalakshmi et.al [85] provided a review of multimodal biometric person authentication systems

with a discussion on modalities, techniques in multimodal biometric system and security aspects

in biometric systems.

2.6. SUMMARY

Until now the Biometric authentication has grown in popularity as a way to provide

personal identification. The research field is blooming with various unimodal and multimodal

biometric system and with various levels of fusion. The research works on hand geometry,

palmprint, knuckle extraction, speech unimodal biometric systems and multimodal biometric

systems are discussed in this chapter.