FACE RECOGNTION USING PCA ALGORITHM

Abstract

As society becoming more and more electronically connected, the capability to

automatically establish an identity of individuals using face as a biometric has become

important. Many applications such as identity verification, criminal face recognition, and

surveillance require robust and accurate face recognition technology. Face recognition has

become a very challenging problem in presence of clutter and variability of the background,

noise and occlusion, and finally speed requirements. This paper focuses on developing a face

recognition system using an extended PCA algorithm. The proposed algorithm uses the concept

of PCA and represents an improved version of PCA to deal with the problem of orientation and

lightening conditions present in the original PCA. The preprocessing phase of the proposed

algorithm emphasize the efficiency of the algorithm even when number of images per person or

the orientation is very different.

Keywords: Face recognition, Principal component Analysis

Table of Contents

Title

1.0 Introduction

2.0 Liturature and Survey and Background Knowledge

2.1 Face Recognition

2.2 Face Recognition using PCA algorithm

2.3 IPCA algorithm

2.4 Result and Discussion

3.0 Advantage and Limitations

3.1 Compared to other technologies

3.1.1Weaknesses

3.1.2Effectiveness

3.1.3Privacy issue

4.0 Applications

4.1 Access controll

4.2 Identification system

4.3 Surveillance

4.4 Pervasive Computing

5.0 Conclusion

References

1.0 INTRODUCTION

Face recognition has been a challenging and quite interesting problem in the field of

pattern recognition for a very long time. Beginning with Bledsoe's [2] and Kanade's [3] early

systems, a number of automated or semi automated face recognition strategies have modeled and

classified faces based on normalized distances and ratios among feature points. Recently this

general approach has been continued and improved by the recent work of Yuille et al [4].

Face recognition has received significant attention in the past decades due to its potential

applications in biometrics, information security, law enforcement, etc. By far, numerous methods

have been suggested to address this problem [3].

Digital images and video are becoming more and more important in the multimedia

information era. The human face is one of the most important objects in an image or video.

Detecting the location of human faces and then extracting the facial features in an image is an

important ability with a wide range of applications, such as human face recognition, surveillance

systems, human computer interfacing, video-conferencing, etc. In an automatic face recognition

system [1], the "rst step is to segment the face in an image or video irrespective of whether the

background is simple or cluttered. For model-based video coding [2], the synthesis performance

is quite dependent on the accuracy of the facial feature extraction process. In other words, a

reliable method for detecting the face regions and locating the facial features is indispensable to

such applications. This paper presents an efficient method for face detection and facial feature

extraction in a cluttered image.

2.0 Liturature and Survey and

Background Knowledge

2.1 FACE RECOGNTION

A facial recognition system is a computer application for automatically identifying or

verifying a person from a digital image or video frame from a video source . One of the ways to

do this is by comparing selected facial features from the image and a facial database .

It is typically used in security system and can be compared to other biometrics such as

fingerprint or eye iris recognition system[6].

Some facial recognition algorithms identify facial features by extracting landmarks, or

features, from an image of the subject's face. For example, an algorithm may analyze the relative

position, size----, and/or shape of the eyes, nose, cheekbones, and jaw [2] These features are then

used to search for other images with matching features [3] Other algorithms normalize a gallery

of face images and then compress the face data, only saving the data in the image that is useful

for face recognition. A probe image is then compared with the face data [4] One of the earliest

successful system [5] is based on template matching technique[6] applied to a set of salient facial

features, providing a sort of compressed face representation.

Recognition algorithms can be divided into two main approaches, geometric, which looks

at distinguishing features, or photometric, which is a statistical approach that distills an image

into values and compares the values with templates to eliminate variances.

Popular recognition algorithms include Principal Component Analysis using Eigen

faced ,Linear Discriminate Analysis , Elastic Bunch Graph Matching using the Fisher face

algorithm , the Hidden Markov Model, the Multilinear Subspace Learning using tensor

representation , and the Neuronal motivated dynamic link matching .

2.1.1 SOFTWARES BASED ON FACE RECOGNITION :

Notable software with face recognition ability include :

1] digiKam (KDE)

2] iPhoto (Apple)

3] OpenCV (Open Source)[10]

4] Photoshop Elements (Adobe Systems)

5] Picasa (Google)

6] Picture Motion Browser (Sony)

7] Windows Live Photo Gallery (Microsoft)

8] Mathematica

2.2 FACE RECOGNITION USING PCA ALGORITHM

Principal component analysis (PCA) turns out to be very effective [2]. Recently, a PCA

closely-related method, independent component analysis (ICA) [3], has also been applied to face

recognition. ICA can be viewed as a generalization of PCA since it concerns not only second-

order dependencies but also high-order dependencies between variables. The previous

researchers [4,5] however, all use the standard PCA as the baseline algorithm to evaluate ICA-

based face recognition systems. The initial success of eigenfaces popularized the idea of

matching images in compressed subspaces. Researchers began to search for other subspaces that

might improve performance. One alternative is Fisher's linear discriminant analysis (LDA, a.k.a.

"fisher faces") [6]. For any N-class classification problem, the goal of LDA is to find the N-1

basis vectors that maximize the interclass distances while minimizing the intraclass distances. At

one level, PCA and LDA are very different: LDA is a supervised learning technique that relies

on class labels, whereas PCA is an unsupervised technique. Nonetheless, in circumstances where

class labels are available either technique can be used, or LDA has been compared to PCA in

several studies [7]. Principal Component Analysis is a standard technique used to approximate

the original data with lower dimensional feature vectors [8]. The basic approach is to compute

the eigenvectors of the covariance matrix, and approximate the original data by a linear

combination of the leading eigenvectors. The mean square error (MSE) in reconstruction is equal

to the sum of remaining Eigen values. The feature vector here is the PCA projection coefficient.

PCA is appropriate when the samples are from one class or group (super class). In real

implementation, there are two ways to compute the eigenvalues and eigenvectors: SVD

decomposition and regular Eigen computation. For efficient way to compute or update the SVD,

please refer to [10]. In many cases, even though the matrix is full rank matrix, the large condition

number will create a numerical problem.

The distance measure used in the matching could be a simple Euclidean or a weighted

Euclidean distance. It has been suggested that the weighted Euclidean will give better

classification than the simple Euclidean distance [10]. Moreover this technique can also be

applied for the purpose of Facial Expression Analysis. Most approaches to automatic facial

expression analysis attempt to recognize a small set of prototypic emotional facial expressions,

i.e., fear, sadness, disgust, anger, surprise, and happiness (e.g., [9, 10]. This practice follows

from the work of Darwin [10], and more recently Ekman [8], who suggested that basic emotions

have corresponding prototypic expression .

2.3 IMPROVED PRINCIPLE COMPONENT ANALYSIS (IPCA)

Principal Component Analysis (PCA) is a way of identifying patterns in data, and

expressing the data in such a way as to highlight their similarities and differences. In PCA the

variance of each image form the mean image is determined. This variance is a measure of

variability in the face space. To apply PCA for face recognition, Eigenfaces are calculated and

the weight of these Eigenfaces is used to find the contribution of training images to the input

image. In the proposed work, certain changes to the original PCA algorithm are made. The

preprocessing of the training images has been done to remove the background, lightening

conditions and the orientation factors. Also, some normalization steps have been included to

remove the calculation induced errors. Our proposed algorithm is an improvement of PCA

algorithm. The original PCA algorithm didn't work well when the orientation of the images was

very large i.e. around 90 degrees. But our proposed algorithm worked quite well even in those

cases in which PCA failed.

2.3.1 The main steps of Improved PCA algorithm are:

1] The faces in the training set are preprocessed by taking the co-ordinates of both the eyes

and mouth and then applying cropping and aligning on these distances.

2] From the preprocessed training set, compute the Eigenfaces and then obtain the best

Eigenfaces corresponding to highest Eigen values.

3] Now project these Eigenfaces into the face database to find their contribution.

4] Take an input image which has to be identified and apply the same preprocessing steps.

5] Find the weight pattern of the Eigenfaces by projecting the input image into Eigen faces.

6] Now reconstruct the input image from the weighted Eigenfaces.

7] Determine if it is a face at all and if so, either known or unknown.

2.4 RESULTS AND DISCUSSION

The experiments were conducted in MATLAB 7.0 taking following parameters: A total

of 110 images were taken in the training set. There were 11 images per person and a total of 10

persons. These 11 images were in the basic 11 directions including the 90 degree orientation.

These images were taken from FERET database which is a standard database meant for testing

various face recognition algorithms. These images were then preprocessed and stored at a

separate place. The input image was also preprocessed to remove the noise from it. The

resolution of the original images was 256*384. After the preprocessing the resolution became

241*291 since only face part of the image was taken.

2.4.1 The two test cases were classified as follows:

1] As the first test case, we took the image of the person who was in the database but the input

image was not included in the database. It had a different orientation which we have taken as 90

degree to test the robustness and efficiency of the algorithm.

2] As the second case, we took the image of the person which was exactly present in the

database.

The Figure 1(b) shows the preprocessing image of input image as shown in Figure 1(a).

Fig. 1 (a) Input Image Fig 1. (b) Preprocessed Image.

2.4.2 Experiment 1: A different image taken as input

As explained above, we took the image of the person who was in the database but the

input image was not included in the database. It had a different orientation, which we have taken

as 90 degree, to test the robustness and efficiency of the algorithm.

Now our algorithm reconstructs the input image by considering the weights if the

Eigenfaces and the contribution of each face. The reconstructed image is shown to the right part

of Figure 2.

Fig. 2 (a) Left part shows input image fig.2. (b) Right part shows Reconstructed Image.

It is evident from Figure 2 , the reconstructed image resembles the preprocessed input

image hence improving the recognition efficiency . The plot of the weight of the input image

against face space and the Euclidean distance of the input image from all of the face space

images is shown in Figure 3. It is clear that the Euclidean distance of image "103.jpg" is the

lowest and below the threshold value. Also the Euclidean distances of the input image form the

face class (all the images of a single person) are comparable.

The face identified by the proposed algorithm is shown in Figure 3a

Fig. 3a. Face identified from database.

2.4.3. Experiment 2:

Input image present exactly in the database

As the second case, we took the image of the person which was exactly present in the

database as shown in Figure 4 (a). The image reconstructed by the algorithm is shown in Figure

4(b).

Fig. 4. (a) Left part shows input image (b) Right part shows Reconstructed Image.

As the input image is exactly same as in the database, so the reconstructed image is very

fine. Now in Figure 6 shown below, we have plotted the Euclidean distance of the input image

from all of the images in the face space. As the image was present exactly, so the Euclidean

image of the same image i.e. "12.jpg" is zero. This is also true theoretically.

The face identified by the proposed algorithm is shown in Figure 5

Fig. 5a. Face identified from database

3.0 ADVANTAGE AND LIMITATIONS

3.1 Compared to other technologies:

Among the different biometric techniques, facial recognition may not be the most reliable

and efficient .However, one key advantage is that it does not require the cooperation of the test

subject to work. Properly designed systems installed in airports, multiplexes, and other public

places can identify individuals among the crowd, without passers-by even being aware of the

system. Other biometrics like fingerprints, iris scans, and speech recognition cannot perform this

kind of mass identification. However, questions have been raised on the effectiveness of facial

recognition software in cases of railway and airport security.

3.1.1 Weaknesses:

Face recognition is not perfect and struggles to perform under certain conditions. Ralph

Gross, a researcher at the Carnegie Mellon Robotics Institute, describes one obstacle related to

the viewing angle of the face: "Face recognition has been getting pretty good at full frontal faces

and 20 degrees off, but as soon as you go towards profile, there've been problems.

Other conditions where face recognition does not work well include poor lighting,

sunglasses, long hair, or other objects partially covering the subject’s face, and low resolution

images[7].

Another serious disadvantage is that many systems are less effective if facial expressions

vary. Even a big smile can render the system less effective. For instance: Canada now allows

only neutral facial expressions in passport photos[9]

There is also inconstancy in the datasets used by researchers. Researchers may use

anywhere from several subjects to scores of subjects, and a few hundred images to thousands of

images. It is important for researchers to make available the datasets they used to each other, or

have at least a standard dataset[10]. On 18 January 2013 Japanese researchers created a privacy

visor that uses nearly infrared light to make the face underneath it unrecognizable to facial

recognition software[11].

3.1.2 Effectiveness:

Critics of the technology complain the London Borough of Newham scheme has, as of

2004, never recognized a single criminal, despite several criminals in the system's database

living in the Borough and the system having been running for several years. "Not once, as far as

the police know, has New ham’s automatic facial recognition system spotted a live target.This

information seems to conflict with claims that the system was credited with a 34% reduction in

crime (hence why it was rolled out to Birmingham also).However it can be explained by the

notion that when the public is regularly told that they are under constant video surveillance with

advanced face recognition technology, this fear alone can reduce the crime rate, whether the face

recognition system technically works or does not. This has been the basis for several other face

recognition based security systems, where the technology itself does not work particularly well

but the user's perception of the technology does.

An experiment in 2002 by the local police department in Tampa, Florida, had similarly

disappointing results. A system at Boston's Logan Airport was shut down in 2003 after failing to

make any matches during a two-year test period

3.1.3 Privacy issues:

Civil rights right organizations and privacy campaigners such as the EFF and

the ACLU express concern that privacy is being compromised by the use of surveillance

technologies. Some fear that it could lead to a “total surveillance society,” with the government

and other authorities having the ability to know the whereabouts and activities of all citizens

around the clock. This knowledge has been, is being, and could continue to be deployed to

prevent the lawful exercise of rights of citizens to criticize those in office, specific government

policies or corporate practices. Many centralized power structures with such surveillance

capabilities have abused their privileged access to maintain control of the political and economic

apparatus, and to curtail populist reforms [10].

Facial recognition can be used not just to identify an individual, but also to unearth other

personal data associated with an individual – such as other photos featuring the individual, blog

posts, social networking profiles, Internet behavior, travel patterns, etc. – all through facial

features alone [10].Moreover, individuals have limited ability to avoid or thwart facial

recognition tracking unless they hide their faces. This fundamentally changes the dynamic of

day-to-day privacy by enabling any marketer, government agency, or random stranger to secretly

collect the identities and associated personal information of any individual captured by the facial

recognition system [4].

Social media web sites such as Facebook have very large numbers of photographs of

people, annotated with names. This represents a database which could be potentially used (or

abused) by governments for facial recognition purposes.

In July 2012, a hearing was held before the Subcommittee on Privacy, Technology and

the Law of the Committee on the Judiciary, United States Senate, to address issues surrounding

what facial recognition technology means for privacy and civil liberties.

4.0 APPLICATION

Many applications for face recognition have been envisaged, and some of them have been

hinted at above. Commercial applications have so far only scratched the surface of the potential.

Installations so far are limited in their ability to handle pose, age and lighting variations, but as

technologies to handle these effects are developed, huge opportunities for deployment exist in

many domains.

4.1 Access Control:

Face verification, matching a face against a single enrolled exemplar, is well within the

capabilities of current Personal Computer hardware. Since PC cameras have become widespread,

their use for face-based PC logon has become feasible, though take-up seems to be very limited.

Increased ease-of-use over password protection is hard to argue with today’s somewhat

unreliable and unpredictable systems, and for few domains is there motivation to progress

beyond the combinations of password and physical security that protect most enterprise

computers. As biometric systems tend to be third party, software add-ons the systems do not yet

have full access to the greater hardware

security guarantees afforded by boot-time and hard disk passwords. Visionics’ face-based screen

lock is one example, bundled with PC cameras. Naturally such PC-based verification systems

can be extended to control authorization for single-sign-on to multiple networked services, for

access to encrypted documents and transaction authorization, though again uptake of the

technology has been slow.

Face verification is being used in kiosk applications, notably inMr. Payroll’s (now

Innoventry) cheque-cashing kiosk with no human supervision. Innoventry claims to have one

million enrolled customers. Automated TellerMachines, already often equipped with a camera,

have also been an obvious candidate for face recognition systems (e.g. Viisage’s FacePIN), but

development seems not to have got beyond pilot schemes. Banks have been very conservative in

deploying biometrics as they risk losing far more through customers disaffected by being falsely

rejected than they might gain in fraud prevention. Customers themselves are reluctant to incur

burdensome additional security measures whentheir personal liability is already limited by law.

For better acceptance, robust passive acquisition systems with very low false rejection

probabilities are necessary.

Physical access control is another domain where face recognition is attractive (e.g. Cognate’s

FaceVACS, Miros’ TrueFace) and here it can even be used in combination with other biometrics.

BioId [3] is a system which combines face recognition with speaker identification and lip

motion.

4.2 Identification Systems:

Two US States (Massachusetts and Connecticut [3]) are testing face recognition for the

policing of Welfare benefits. This is an identification task, where any new applicant being

enrolled must be compared against the entire database of previously enrolled claimants, to ensure

that they are not claiming under more than one identity. Unfortunately face recognition is not

currently able to reliably identify one person among the millions enrolled in a single state’s

database, so demographics (zip code, age, name etc. ) are used to narrow the search (thus

limiting its effectiveness), and human intervention is required to review the false alarms that

such a system will produce. Here a more accurate system such as fingerprint or iris-based person

recognition is more technologically appropriate, but face recognition is chosen because it is more

acceptable and less intrusive. In Connecticut, face recognition is the secondary biometric added

to an existing fingerprint identification system. Several US States, including Illinois, have also

instituted face recognition for ensuring that people do not obtain multiple driving licenses.

4.3 Surveillance:

The application domain where most interest in face recognition is being shown is

probably surveillance. Video is the medium of choice for surveillance because of the richness

and type of information that it contains and naturally, for applications that require identification,

face recognition is the best biometric for video data. Though gait or lip motion recognition have

some potential. Face recognition can be applied without the subject’s active participation, and

indeed without the subject’s knowledge. Automated face recognition can be applied ‘live’ to

search for a watch-list of ‘interesting’ people, or after the fact using surveillance footage of a

crime to search through a database of suspects. The deployment of face-recognition surveillance

systems has already begun

, though the technology is not accurate enough yet [4]. The US government is investing in

improving this technology [10] and while useful levels of recognition accuracy may take some

time to achieve, technologies such as multiple steerable zoom cameras, non-visible wavelengths

and advanced signal processing are likely to bring about super-human perception in the data

gathering side of surveillance systems.

4.4 Pervasive Computing:

Another domain where face recognition is expected to become very important, although

it is not yet commercially feasible, is in the area of pervasive or ubiquitous computing. Many

people are envisaging the pervasive deployment of information devices. Computing devices,

many already equipped with sensors, are already found throughout our cars and in many

appliances in our homes, though they will become ever more widespread. All of these devices

are just now beginning to be networked together. We can envisage a future where many

everyday objects have some computational power,

Allowing them to adapt their behavior —to time, user, user control and a host of other factors.

The communications infrastructures permitting such devices to communicate to one another are

being defined and developed (e.g. Bluetooth, IEEE 802.11). So while it is easy to see that the

devices will be able to have a well-understood picture of the virtual world with information

being shared among many devices, it is less clear what kind of information these devices will

have about the real physical world. active commands on the part of the user. Some simple

devices can sense the environment, but it will be increasingly important for such pervasive,

networked computing devices to know about the physical world and the people within their

region of interest. Only by making the pervasive infrastructure ‘human aware’ can we really reap

the benefits of productivity, control and ease-of-use that pervasive computing promises. One of

the most important parts of human awareness is knowing the identity of the users close to a

device, and while there are other biometrics that can contribute to such knowledge, face

recognition is the most appropriate because of its passive nature. There are many examples of

pervasive face recognition tasks: Some devices such as Personal Digital Assistants (PDAs) may

already contain cameras for other purposes, and in good illumination conditions will be able to

identify their users. A domestic message centre may have user personalization that depends on

identification driven by a built-in camera. Some pervasive computing environments may need to

know about users when not directly interacting with a device, and may be made ‘human aware’

by a network of cameras able to track the people in the space and identify each person, as well as

have some understanding of the person’s activities. Thus a video conference room could steer the

camera and generate a labeled transcript of the conference; an automatic lobby might inform

workers of specific visitors; and mobile workers could be located and kept in touch by a system

that could identify them and redirect phone calls.

5.0 CONCLUSION

This paper presents an algorithm to recognize faces present in the face database. The

proposed algorithm uses the concept of PCA and represents an improved version of PCA to deal

with the problem of orientation and lightening conditions present in the original PCA. In the case

when test image was in the database, the person was identified correctly. Now even when we

took as input, an image with different orientation of a person present in database, the algorithm

successfully identified the person. This shows that pre-processing greatly enhances the efficiency

of the algorithm even when we have less number of images per person or the orientation is

greatly different. This work is being extended to deal with a range of aspects (other than full

frontal views) by defining a small number of classes for each known person corresponding to

characteristic views.

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