current trends on face recognition …gmail.com, [email protected],...
TRANSCRIPT
International Journal of Computer Engineering and Applications,
Volume XII, Issue I, Jan. 18, www.ijcea.com ISSN 2321-3469
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 153
CURRENT TRENDS ON FACE RECOGNITION METHODS IN
FACE BIOMETRICS
Manjunatha Hiremath1, Manukumar N.M1, Venkateswari2 1 Department of Computer Science, Christ University, Bangalore, 560029
2Research Assistant, Christ University, Bangalore, 560029
[email protected], [email protected], [email protected]
ABSTRACT:
Like all biometrics solutions, face recognition technology measures and matches the unique characteristics for the purposes of identification or authentication. Often leveraging a digital or connected camera, facial recognition software can detect faces in images, quantify their features, and then match them against stored templates in a database. Facial recognition doesn’t just deal with hard identities, but also has the ability to gather demographic data on crowds. This has made face biometrics solutions much sought after in the retail marketing industry. In this paper , we presented a comprehensive review of Face recognition system and the face datasets used in the face biometric.
Keywords: Face Recognition, PCA, ICA, LDA, Kernel, EBGM, EP, Trace transform, AAM, 3D
[1] INTRODUCTION
Facial recognition is a type of biometric procedure that can identify a specific individual in
a digital image by analysing and comparing patterns. So many computer users are notoriously apt
to use poor, easily guessed credentials (such as password), resulting in break-ins where intruders
can guess another user’s credentials and gain unauthorized access to a digital system. The term
biometrics can resolve all type of credential problems by requiring an additional credential
something associated with the person’s own body. The primary intention of this application is to
prevent the frauds from accessing secured resources. This Biometrics offers reliable solution
CURRENT TRENDS ON FACE RECOGNITION METHODS IN FACE BIOMETRICS
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 154
through some technologies. As part of biometrics, the face recognition plays a very important role.
A facial recognition system is a computer application capable of identifying or verifying a person
from a digital image or a video frame from a video source. In this article, we discussed so many
state of the art face recognition procedures with their performance.
[2] FACE RECOGNITION METHODOLOGIES
A. Principal Component Analysis (PCA)
It is one of the statistical technique for reducing the dimensionality of the dataset, and possibly
to feature selection. PCA based algorithm have been the base of the several research projects in
computer vision. Basically PCA is the unsupervised learning technique used for the producing the
optimal linear least square decomposition of a datasets. PCA is a typical de correlation repetition in
present research, one originates an orthogonal projection basis which directly leads to
dimensionality reduction for classification problems.
B. Independent Component Analysis (ICA)
There are a number of algorithms for performing ICA [11], [13], [14], [25]. Author chose the
infomax algorithm proposed by Bell and Sejnowski [11], which was derived from the principle of
optimal information transfer in neurons with sigmoidal transfer functions [27]. The algorithm is
motivated as follows: Let X be an -dimensional (n -D) random vector representing a distribution
of inputs in the environment. (Here, boldface capi-tals denote random variables, whereas plain text
capitals denote matrices). Let W be an n×n invertible matrix, U=WX and Y=f(U) an n-D random
variable representing the outputs of n-neurons. Each component of f = (f1,….,fn) is an invertible
squashing function, mapping real numbers into the interval. Typically, the logistic function is
used
fi (u) = . (1)
The U1.,..,Un variables are linear combinations of inputs and can be interpreted as presynaptic
activations of -neurons. The Y1.,..,Yn variables can be interpreted as postsynaptic activa-tion rates
and are bounded by the interval . The goal in Bell and Sejnowski’s algorithm is to maximize the
mutual informa-tion between the environment X and the output of the neural network Y . This is
achieved by performing gradient ascent on the entropy of the output with respect to the weight
matrix . The gradient update rule for the weight matrix, is as follows:
of the nonlinear where transfer function is the same as the cumulative density
functions of the underlying ICs (up to scaling and translation) it can be shown that maximizing the
joint entropy of the outputs in also minimizes the mutual information be-tween the individual
outputs in [12], [42]. In practice, the logistic transfer function has been found sufficient to separate
mixtures of natural signals with sparse distributions including sound sources [11].
The algorithm is speeded up by including a “sphering” step prior to learning [12]. The row means
of are subtracted, and then is passed through the whitening matrix, which is twice the inverse
square root2 of the covariance matrix
International Journal of Computer Engineering and Applications,
Volume XII, Issue I, Jan. 18, www.ijcea.com ISSN 2321-3469
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 155
This removes the first and the second-order statistics of the data; both the mean and covariances are
set to zero and the variances are equalized. When the inputs to ICA are the “sphered” data, the full
transform matrix is the product of the sphering ma-trix and the matrix learned by ICA
C. Linear Discriminant Analysis (LDA):
Both PCA and LDA have been used for face recognition [5, 6, 7, 8, 15, 16, 11]. With PCA, the3
input face images usually needed to be w.arped to a standard face because of the large within-class
variance [6, 7]. This processing stage reduces the within-class variance dramatically, thus
improving the recognition rate. Author first built a simple system based on pure LDA [8], but the
performance was not satisfactory on a large dataset of person not present in the training set. The
idea of combining PCA and LDA has been previously explored by Weng et al [15]. Although the
pure LDA algorithm does not have any problem discriminating the trained samples, Author have
observed that it does not perform very well for the following three cases:
• When the testing samples are from persons not in the training set
• When markedly different samples of trained classes are presented
• Samples with different background are presented
• Basically this is a generalization problem since the pure LDA based system is very much tuned to
the specific training set, which has the same number of classes as persons, with 2 or 4 samples per
class!
Combining PCA and LDA, Author obtain a linear projection which maps the input image x first
into the face-subspace y, and then into the classification space z:
y = T X (6) z = WyT y
(7) z = WxT x (8)
where is the PCA transform, Wy is the best linear discriminating transform on PCA feature
space, and Wx is the composite linear projection from the original image space to the classification
space. After this composite linear projection, recognition is performed in the classification space
based on some distance measure criterion.
One thing need to be pointed out is that for the im-plementation of PCA, in many cases, even though
the covariance matrix is a full-rank matrix, the large con-dition number will create a numerical
problem. One way around this is to compute the eigenvalues andeigenvectors for C+I instead of C,
where is a pos-itive number. This is based on the following lemma:Lemma 1 Matrices C and C + I
have same eigen-vectors but different eigenvalues with the relationship: C+I = + as long as + is
not equal to zero.
Performance improvement of this method over pure LDA based method is demonstrated through
our own experiments and FERET test. Author believe that by combining PCA and LDA, using PCA
to construct a task-specific sub-space and then applying LDA on that subspace, other image
recognition systems such as fingerprint, optical character recognition can be improved. Author will
study the subspace-LDA approach in detail and explore the possible applications in future work.
CURRENT TRENDS ON FACE RECOGNITION METHODS IN FACE BIOMETRICS
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 156
D. Eigenspace-based adaptive approach (EP) :
The task of EP is to search for a face basis through the rotated axes defined in a properly whitened
PCA space. Evolution is driven by a fitness function defined in terms of performance accuracy and
class separation (scatter index). Accuracy indicates the extent to which learning has been successful
so far, while the scatter index gives an indication of the expected fitness on future trials. Together,
the accuracy and the scatter index give an indication of the overall performance ability. In analogy
to the statistical learning theory [43], the scatter index is the conceptual analog for the capacity of
the classifier and its use is to prevent overfitting. By combining these two terms together (with
proper weights), GA can evolve balanced results and yield good recognition performance and
generalization abilities.
One should also point out that just using more principal components (PCs) does not necessarily lead
to better performance, since some PCs might capture the within class scatter which is unwanted for
the purpose of face recognition [25],[27]. In proposed method, one can search the 20- and 30-
dimensional whitened PCA spaces corresponding to the leading eigenvalues, since it is in those
spaces that most of the variations characteristic of human faces occur.
In analogy to pursuit methods, EP seeks to learn an optimal basis for the dual purpose of data
compression and pattern classification. The challenge for EP is to increase the generalization ability
of the learning machine as a result of seeking the trade-off between minimizing the empirical risk
encountered during training and narrowing the confidence interval for reducing the guaranteed risk
for future testing on unseen images. Towards that end, EP implements strategies characteristic of
genetic algorithms (GAs) for searching the space of possible solutions and determining an optimal
basis. Within the face recognition framework, EP seeks an optimal basis for face projections suitable
for compact and efficient face encoding in terms of both present and future recognition ability.
Experimental results, using a large and varied subset from the FERET facial database, show that
the EP method compares favorably against two popular methods for face recognition Eigenfaces
and Fisherfaces.
E. Elastic Bunch Graph Matching (EBGM) :
A first set of graphs is generated manually. Nodes are located at fiducial points and edges between
the nodes as well as correspondences between nodes of different poses are defined. Once the system
has an FBG (possibly consisting of only one manually defined model), graphs for new images can
be generated automatically by elastic bunch graph matching. Initially, when the FBG contains only
few faces, it is necessary to review and correct the resulting matches, but once the FBG is rich
enough (approximately 70 graphs) one can rely on the matching and generate large galleries of
model graphs automatically. Matching a FBG on a new image is done by maximizing a graph
similarity between an image graph and the FBG of identical pose. It depends on the jet similarities
and a topography term, which takes into account the distortion of the image grid relative to the FBG
grid.
International Journal of Computer Engineering and Applications,
Volume XII, Issue I, Jan. 18, www.ijcea.com ISSN 2321-3469
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 157
Fig. 1. The Face Bunch Graph (FBG) serves as a general representation of faces. Each stack of
discs represents a jet. From a bunch of jets attached to a single node only the best fitting one is
selected for a match, indicated by gray shading.
Fig. 2. Object-adapted grids for different poses. The nodes are positioned automatically by elastic
bunch graph matching.
.
The system presented is general and flexible. It is designed for an in-class recognition task, i.e., for
recognizing members of a known class of objects. Authors have applied it to face recognition but
the system is in no way specialized to faces and author assume that it can be directly applied to
other in-class recognition tasks, such as recognizing individuals of a given animal species, given
the same level of standardization of the images. In contrast to many neural network systems, no
extensive training for new faces or new object classes is required. Only a moderate number of
typical examples have to be inspected to build up a bunch graph, and individuals can then be
recognized after storing a single image.
The system presented is general and flexible. It is designed for an in-class recognition task, i.e., for
recognizing members of a known class of objects. Authors have applied it to face recognition but
the system is in no way specialized to faces and author assume that it can be directly applied to
other in-class recognition tasks, such as recognizing individuals of a given animal species, given
the same level of standardization of the images. In contrast to many neural network systems, no
extensive training for new faces or new object classes is required. Only a moderate number of
typical examples have to be inspected to build up a bunch graph, and individuals can then be
recognized after storing a single image.
Author tested the system with respect to rotation in depth and differences in facial expression. Some
experiments included mirror reflection. Author did not investigate robustness to other variations,
such as illumination changes or structured background. The performance is high on faces of same
CURRENT TRENDS ON FACE RECOGNITION METHODS IN FACE BIOMETRICS
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 158
pose. Author also showed robustness against rotation in depth up to about 22$. For large rotation
angles the performance degrades significantly. Our system perfoms well compared to other systems.
Results of a blind test of different systems on the FERET database were published in [6] and [7].
The Trace transform, a generalization of the Radon transform, is a new tool for image processing
which can be used for recognizing objects under transformations, e.g. rotation, translation and
scaling. To produce the Trace transform one computes a functional along tracing lines of an image.
Different Trace transforms can be produced from an image using different trace functionals.
The Trace transform [1], a generalization of the Radon transform, is a new tool for image processing
which can be used for recognizing objects under transformations, e.g. rotation, translation and
scaling. To produce the Trace transform one computes a functional along tracing lines of an image.
Each line is characterized by two parameters, namely its distance p
from the centre of the axes and
the orientation the normal to the line has with respect to the reference direction. In addition,
Author define parameter t along the line with its origin at the foot of the normal. The definitions of
these three parameters are shown in figure 1. The image is transformed to another image with the
Trace transform which is a 2-D function depending on parameters ( , p)
. Different Trace
transforms can be produced from an image using different trace functionals. An example of the
Trace transform is shown in figure 2. It is shown that the image space in the x and
y directions is
transformed to the Trace transform space in the and p
directions.
Fig. 3. Tracing line on an image with parameters , p
and t
.
Fig. 4. An image and its Trace transform.
International Journal of Computer Engineering and Applications,
Volume XII, Issue I, Jan. 18, www.ijcea.com ISSN 2321-3469
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 159
One of the key properties of the Trace transform is that it can be used to construct features invariant
to rotation, translation and scaling. Author should point out that invariance to rotation and scaling
is harder to achieve than invariance to translation. Let us assume that an object is subjected to linear
distortions, i.e. rotation, translation and scaling. It is equivalent to saying that the image remains the
same but viewed from a linearly distorted coordinate system. Consider scanning an image with lines
in all directions. Let us denoted the set of all these lines with . The Trace transform is a function
g defined on with the help of T which is some functional of the image function when it is
considered as a function of variable t . T is called the trace functional.
g( , p) T F( ( , p t, )), (1) where F( , p t, )
stands for the values of the image function
along the chosen line. Parameter t is eliminated after taking the trace functional. The result is
therefore a 2-D function of parameters and p
and can be interpreted as another image defined on
.
F. Active Appearance Model (AAM) :
An Active Appearance Model (AAM) is an integrated statistical model which combines a model of
shape variation with a model of the appearance variations in a shape-normalized frame. An AAM
contains a statistical model if the shape and gray-level appearance of the object of interest which
can generalize to almost any valid example. Matching to an image involves finding model
parameters which minimize the difference between the image and a synthesized model example
projected into the image.
G. 3-D Face Recognition:
Human face is a surface lying in the 3-D space intrinsically. Therefore the 3-D model should be
better for representing faces, especially to handle facial variations, such as pose, illumination etc.
Blantz et al. proposed a method based on a 3-D morphable face model that encodes shape and
texture in terms of model parameters, and algorithm that recovers these parameters from a single
image of a face.
The main novelty of this approach is the ability to compare surfaces independent of natural
deformations resulting from facial expressions. First, the range image and the texture of the face are
acquired. Next, the range image is preprocessed by removing certain parts such as hair, which can
complicate the recognition process. Finally, a canonical form of the facial surface is computed. Such
a representation is insensitive to head orientations and facial expressions, thus significantly
simplifying the recognition procedure. The recognition itself is performed on the canonical surfaces.
H. Bayesian Framework :
A probabilistic similarity measure based on Bayesian belief that the image intensity differences are
characteristic of typical variations in appearance of an individual. Two classes of facial image
variations are defined: intrapersonal variations and extrapersonal variations. Similarity among faces
is measures using Bayesian rule.
CURRENT TRENDS ON FACE RECOGNITION METHODS IN FACE BIOMETRICS
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 160
All of the face recognition systems cited above (indeed the majority of the face recognition system
published in the open literature) relay on similarity matrics which are invariably based on Euclidean
distance or normalized correlation, thus corresponding to standard “template-matching”-i.e,,.,
nearest-neighbour based recognition. For example, simplest form the similarity measure S I I( 1, 2)
between two facial images I1 and I2
can be set to be inversely proportional to the norm
I. Support Vector Machine (SVM) :
Given a set of points belonging to two classes, a Support Vector Machine (SVM) finds the
hyperplane that separates the largest possible fraction of points of the same class on the same side,
while maximizing the distance from either class to the hyperplane. PCA is first used to extract
features of face images and then discrimination functions between each pair of images are learned
by SVMs.
J. Hidden Markov Models (HMM) :
Hidden Markov Models (HMM) are a set of statistical models used to characterize the statistical
properties of a signal. HMM consists of two interrelated processes: (1) an underlying, unobservable
Markov chain with a finite number of states, a state transition probability matrix and an initial state
probability distribution and (2) a set of probability density functions associated with each state.
K. Boosting & Ensemble Solutions:
The idea behind Boosting is to sequentially employ a weak learner on a weighted version of
a given training sample set to generalize a set of classifiers of its kind. Although any individual
classifier may perform slightly better than random guessing, the formed ensemble can provide a
very accurate (strong) classifier. Viola and Jones build the first real-time face detection system by
using AdaBoost, which is considered a dramatic breakthrough in the face detection research. On
the other hand, papers by Guo et al. are the first approaches on face recogntion using the AdaBoost
methods.
[3] DATA SETS
In biometrics when benchmarking a procedure it is suggested to use a standard test data set for
researchers to be able to directly compare the results. In the state of the art literature there are
numerous datasets in use at present, the choice of an appropriate database to be used should be made
based on the task given (aging, expressions, lighting etc). The other method to choose the data set
specific to the property to be tested (e.g. how algorithm behaves when given images with lighting
deviations or images with diverse facial expressions). Some of the benchmark datasets are given
below:
The Color FERET Database, USA
SCface - Surveillance Cameras Face Database
SCfaceDB Landmarks
Multi-PIE
The Yale Face Database
1 2 I I
International Journal of Computer Engineering and Applications,
Volume XII, Issue I, Jan. 18, www.ijcea.com ISSN 2321-3469
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 161
The Yale Face Database B
PIE Database, CMU
Project - Face In Action (FIA) Face Video Database, AMP, CMU
AT&T "The Database of Faces" (formerly "The ORL Database of Faces")
Cohn-Kanade AU Coded Facial Expression Database
MIT-CBCL Face Recognition Database
Image Database of Facial Actions and Expressions - Expression Image Database
Face Recognition Data, University of Essex, UK
NIST Mugshot Identification Database
NLPR Face Database
M2VTS Multimodal Face Database (Release 1.00
The Extended M2VTS Database, University of Surrey, UK
The AR Face Database, The Ohio State University, USA
The University of Oulu Physics-Based Face Database
CAS-PEAL Face Database
Japanese Female Facial Expression (JAFFE) Database
BioID Face DB - HumanScan AG, Switzerland
Psychological Image Collection at Stirling (PICS)
The Sheffield Face Database (previously: The UMIST Face Database)
Face Video Database of the Max Planck Institute for Biological Cybernetics
Caltech Faces
EQUINOX HID Face Database
VALID Database
The UCD Colour Face Image Database for Face Detection
Georgia Tech Face Database
Indian Face Database
VidTIMIT Database
Labeled Faces in the Wild
The LFWcrop Database
Labeled Faces in the Wild-a (LFW-a)
3D_RMA database
GavabDB: 3D face database, GAVAB research group, Universidad Rey Juan Carlos,
Spain
FRAV2D Database
FRAV3D Database
BJUT-3D Chinese Face Database
The Bosphorus Database
PUT Face Database
The Basel Face Model (BFM)
Plastic Surgery Face Database
The Iranian Face Database (IFDB)
The Hong Kong Polytechnic University NIR Face Database
The Hong Kong Polytechnic University Hyperspectral Face Database (PolyU-HSFD)
MOBIO - Mobile Biometry Face and Speech Database
CURRENT TRENDS ON FACE RECOGNITION METHODS IN FACE BIOMETRICS
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 162
Texas 3D Face Recognition Database (Texas 3DFRD)
Natural Visible and Infrared facial Expression database (USTC-NVIE)
FEI Face Database
ChokePoint
UMB database of 3D occluded faces
VADANA: Vims Appearance Dataset for facial ANAlysis
MORPH Database (Craniofacial Longitudinal Morphological Face Database)
Long Distance Heterogeneous Face Database (LDHF-DB)
PhotoFace: Face recognition using photometric stereo
The EURECOM Kinect Face Dataset (EURECOM KFD)
YouTube Faces Database
YMU (YouTube Makeup) Dataset
VMU (Virtual Makeup) Dataset
MIW (Makeup in the "wild") Dataset
3D Mask Attack Database (3DMAD)
Senthilkumar Face Database (Version 1.0)
McGill Real-world Face Video Database
SiblingsDB Database
FaceScrub - A Dataset With Over 100,000 Face Images of 530 People
LFW3D and Adience3D sets
Indian Movie Face database (IMFDB)
Labeled Wikipedia Faces (LWF)
10k US Adult Faces Database
Denver Intensity of Spontaneous Facial Action (DISFA) Database
BU-3DFE Database (Static Data)
BU-4DFE Database (Dynamic Data)
BP4D-Spontanous Database
CAFE - The Child Affective Face Set
UFI - Unconstrained Facial Images
Senthil IRTT Face Database Version 1.1
Senthil IRTT Face Database Version1.2
Senthil IRTT Video Face Database 1.0
VT-AAST Bench-marking Dataset
SEAS-FR-DB (School of Engineering & Applied Science - Face Video Database).
[4] CONCLUSION
Face recognition is a necessity of the modern age as the need for identification of individual has
increased with the globalization of the world. Personal authentication through face has been under
research since last two decades. The performance of the face recognition system has been enhanced
using various algorithms. A generic facial authentication method contains three major steps i.e. face
detection, facial features segmentation and face recognition. There are many commonly used
algorithms used for this purpose. This paper provides an overview of different face recognition
approaches. These approaches are categorized into four classes in this paper. These are holistic
based approach, model based approach, hybrid based approach and feature based approach. Various
techniques introduced in each of these categories are discussed.
International Journal of Computer Engineering and Applications,
Volume XII, Issue I, Jan. 18, www.ijcea.com ISSN 2321-3469
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 163
REFERENCES
[1] M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neurosicence, Vol. 3, No. 1, 1991,
pp. 71-86.
[2] M.A. Turk, A.P. Pentland, Face Recognition Using Eigenfaces, Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, 3-6 June 1991, Maui, Hawaii, USA, pp. 586-591.
[3] A. Pentland, B. Moghaddam, T. Starner, View-Based and Modular Eigenspaces for Face Recognition,
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 21-23 June 1994, Seattle,
Washington, USA, pp. 84-91.
[4] H. Moon, P.J. Phillips, Computational and Performance aspects of PCA-based Face Recognition Algorithms,
Perception, Vol. 30, 2001, pp. 303-321.
[5] M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, Face Recognition by Independent Component Analysis, IEEE
Trans. on Neural Networks, Vol. 13, No. 6, November 2002, pp. 1450-1464
[6] K. Etemad, R. Chellappa, Discriminant Analysis for Recognition of Human Face Images, Journal of the
Optical Society of America A, Vol. 14, No. 8, August 1997, pp. 1724-1733
[7] P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, Eigenfaces vs. Fisherfaces: Recognition using Class Specific
Linear Projection, Proc. of the 4th European Conference on Computer Vision, ECCV'96, 15-18 April 1996,
Cambridge, UK, pp. 45-58
[8] W. Zhao, R. Chellappa, A. Krishnaswamy, Discriminant Analysis of Principal Components for Face
Recognition, Proc. of the 3rd IEEE International Conference on Face and Gesture Recognition, FG'98, 14-16
April 1998, Nara, Japan, pp. 336-341
[9] A.M. Martinez, A.C. Kak, PCA versus LDA, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.
23, No. 2, 2001, pp. 228-233
[10] W. Zhao, A. Krishnaswamy, R. Chellappa, D.L. Swets, J. Weng, Discriminant Analysis of
Principal Components for Face Recognition, Face Recognition: From Theory to Applications, H. Wechsler,
P.J. Phillips, V. Bruce, F.F. Soulie, and T.S. Huang, eds., Springer-Verlag, Berlin, 1998, pp. 73-85
[11] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, Face Recognition Using LDA-Based Algorithms, IEEE Trans.
on Neural Networks, Vol. 14, No. 1, January 2003, pp. 195-200
[12] C. Liu, H. Wechsler, Evolutionary Pursuit and Its Application to Face Recognition, IEEE Trans. on Pattern
Analysis and Machine Intelligence, Vol. 22, No. 6, June 2000, pp. 570-582
[13] C. Liu, H. Wechsler, Face Recognition Using Evolutionary Pursuit, Proc. of the Fifth European Conference
on Computer Vision, ECCV'98, Vol II, 02-06 June 1998, Freiburg, Germany, pp. 596-612
[14] L. Wiskott, J.-M. Fellous, N. Krueuger, C. von der Malsburg, Face Recognition by Elastic Bunch Graph
Matching, Chapter 11 in Intelligent Biometric Techniques in Fingerprint and Face Recognition, eds. L.C. Jain
et al., CRC Press, 1999, pp. 355-396
[15] L. Wiskott, J.-M. Fellous, N. Krueuger, C. von der Malsburg, Face Recognition by Elastic Bunch Graph
Matching, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 775-779
[16] M.-H. Yang, Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel
Methods, Proc. of the Fifth IEEE International Conference on Automatic Face and Gesture
Recognition, 20-21 May 2002, Washington D.C., USA, pp. 215-220
[17] F.R. Bach, M.I. Jordan, Kernel Independent Component Analysis, Journal of Machine Learning Research,
Vol. 3, 2002, pp. 1-48
[18] B. Scholkopf, A. Smola, K.-R. Muller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem,
Technical Report No. 44, December 1996, 18 pages
CURRENT TRENDS ON FACE RECOGNITION METHODS IN FACE BIOMETRICS
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 164
[19] M.-H. Yang, Face Recognition Using Kernel Methods, Advances in Neural Information Processing Systems,
T. Diederich, S. Becker, Z. Ghahramani, Eds., 2002, vol. 14, 8 pages
[20] S. Zhou, R. Chellappa, B. Moghaddam, Intra-personal kernel space for face recognition, Proc. of the 6th
International Conference on Automatic Face and Gesture Recognition, FGR2004, 17-19 May 2004, Seoul,
Korea, pp. 235-240
[21] S. Zhou, R. Chellappa, Multiple-exemplar discriminant analysis for face recognition, Proc. of the 17th
International Conference on Pattern Recognition, ICPR'04, 23-26 August 2004, Cambridge, UK, pp. 191-194
[22] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, Face Recognition Using Kernel Direct Discriminant Analysis
Algorithms, IEEE Trans. on Neural Networks, Vol. 14, No. 1, January 2003, pp. 117-126
[23] A. Kadyrov, M. Petrou, The Trace Transform and Its Applications, IEEE Transactions on Pattern Analysis
and Machine Intelligence, Vol. 23, No. 8, August 2001, pp. 811-828
[24] S. Srisuk, M. Petrou, W. Kurutach and A. Kadyrov, Face Authentication using the Trace Transform,
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(CVPR'03), 16-22 June 2003, Madison, Wisconsin, USA, pp. 305-312
[25] S. Srisuk and W. Kurutach, Face Recognition using a New Texture Representation of Face
Images, Proceedings of Electrical Engineering Conference, Cha-am, Thailand, 06-07 November 2003, pp.
1097-1102
[26] T.F. Cootes, C.J. Taylor, Statistical Models of Appearance for Computer Vision, Technical Report, University
of Manchester, 125 pages
[27] T.F. Cootes, K. Walker, C.J. Taylor, View-Based Active Appearance Models, Proc. of the IEEE International
Conference on Automatic Face and Gesture Recognition, 26-30 March 2000, Grenoble, France, pp. 227-232
[28] J. Huang, B. Heisele, V. Blanz, Component-based Face Recognition with 3D Morphable Models, Proc. of the
4th International Conference on Audio- and Video-Based Biometric
Person Authentication, AVBPA 2003, 09-11 June 2003, Guildford, UK, pp. 27-34
[29] V. Blanz, T. Vetter, A Morphable Model for the Synthesis of 3D Faces, Proc. of the SIGGRAPH'99, 08-13
August 1999, Los Angeles, USA, pp. 187-194
[30] V. Blanz, T. Vetter, Face Recognition Based on Fitting a 3D Morphable Model, IEEE Transactions on Pattern
Analysis and Machine Intelligence, Vol. 25, No. 9, September 2003, pp. 1063-1074
[31] B. Moghaddam, J.H. Lee, H. Pfister, R. Machiraju, Model-Based 3D Face Capture with Shape-from-
Silhouettes, Proc. of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures,
AMFG, 17 October 2003, Nice, France, pp. 20-27
[32] J. Lee, B. Moghaddam, H. Pfister, R. Machiraju, Finding Optimal Views for 3D Face Shape
Modeling, Proc. of the International Conference on Automatic Face and Gesture Recognition,
FGR2004, 17-19 May 2004, Seoul, Korea, pp. 31-36
[33] A. Bronstein, M. Bronstein, R. Kimmel, and A. Spira. 3D face recognition without facial surface
reconstruction, in Proceedings of ECCV 2004, Prague, Czech Republic, May 11-14, 2004
[34] A. Bronstein, M. Bronstein, and R. Kimmel, Expression-invariant 3D face recognition, Proc. Audio & Video-
based Biometric Person Authentication (AVBPA), Lecture Notes in Comp. Science 2688, Springer, 2003, pp.
62-69
[35] B. Moghaddam, T. Jebara, A. Pentland, Bayesian Face Recognition, Pattern Recognition, Vol. 33, Issue 11,
November 2000, pp. 1771-1782
[36] C. Liu, H. Wechsler, A Unified Bayesian Framework for Face Recognition, Proc. of the 1998
IEEE International Conference on Image Processing, ICIP'98, 4-7 October 1998, Chicago, Illinois, USA, pp.
151-155
International Journal of Computer Engineering and Applications,
Volume XII, Issue I, Jan. 18, www.ijcea.com ISSN 2321-3469
Manjunatha Hiremath, Manukumar N.M, and Venkateswari 165
[37] B. Moghaddam, C. Nastar, A. Pentland, A Bayesian Similarity Measure for Deformable Image Matching,
Image and Vision Computing, Vol. 19, Issue 5, May 2001, pp. 235-244
[38] G. Guo, S.Z. Li, K. Chan, Face Recognition by Support Vector Machines, Proc. of the IEEE International
Conference on Automatic Face and Gesture Recognition, 26-30 March 2000, Grenoble, France, pp. 196-201
[39] B. Heisele, P. Ho, T. Poggio, Face Recognition with Support Vector Machines: Global versus Component-
based Approach, Proc. of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vol.
2, 09-12 July 2001, Vancouver, Canada, pp. 688-694
[40] K. Jonsson, J. Matas, J. Kittler, Y.P. Li, Learning Support Vectors for Face Verification and Recognition,
Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition, 26-30 March 2000,
Grenoble, France, pp. 208-213
[41] A.V. Nefian, M.H. Hayes III, Hidden Markov Models for Face Recognition, Proc. of the IEEE International
Conference on Acoustics, Speech, and Signal Processing, ICASSP'98, Vol. 5, 12-15 May 1998, Seattle,
Washington, USA, pp. 2721-2724
[42] A.V. Nefian, M.H. Hayes, Maximum likelihood training of the embedded HMM for face detection and
recognition, Proc. of the IEEE International Conference on Image Processing, ICIP 2000, Vol. 1, 10-13
September 2000, Vancouver, BC, Canada, pp. 33-36
[43] A.V. Nefian, Embedded Bayesian networks for face recognition, Proc. of the IEEE
International Conference on Multimedia and Expo, Vol. 2, 26-29 August 2002, Lusanne, Switzerland, pp.
133-136
[44] Y. Freund, R.E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to
Boosting, Journal of Computer and System Sciences, Vol. 55, No. 1, 1997, pp. 119-139
[45] R. Meir, G. Raetsch. An Introduction to Boosting and Leveraging, In S. Mendelson and A. Smola, Editors,
Advanced Lectures on Machine Learning, LNAI 2600, pp. 118-183, Springer, 2003
[46] P. Viola, M.J. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, Vol. 57,
No. 2, May 2004, pp. 137-154
[47] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, S.Z. Li, Ensemble-based Discriminant Learning with Boosting
for Face Recognition, IEEE Transactions on Neural Networks, Vol.
17, No. 1, January 2006, pp. 166-178
[48] G.-D. Guo, H.-J. Zhang, S.Z. Li, Pairwise Face Recognition, Proc. of the Eighth IEEE
International Conference on Computer Vision, ICCV 2001, Vol. 2, 09-12 July 2001, Vancouver, Canada, pp.
282-287
[49] G.-D. Guo, H.-J. Zhang, Boosting for Fast Face Recognition, Second International Workshop on Recognition,
Analysis and Tracking of Faces and Gestures in Real-time Systems, RATFG-
RTS'01, in conjunction with ICCV 2001, 13 July 2001, Vancouver, Canada, pp. 96-100;