Face Recognition using Principle Component

Face Recognition using Principle Component Analysis and Linear Discriminant Analysis Comparative Study S. B. Dabhade∗ , Y. S. Rode, M. M. Kazi, R. R. Manza and K. V. Kale Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS), India.

Abstract. Biometrics is a system in which we used to recognise human on the basis of its physical or behavioural characteristics. Today all over the world every country wants security of data, physical access, etc. Face recognition is widely accepted technique in human being same things are used in computer vision by using the image processing. In this paper we have used Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for extraction of features. Techniques has been applied for identification of a person on various databases such as ORL, Indian & KVKR and distance is calculated by using Euclidian Distance between training images and testing images. In this experiment total 1266 images used apart from that 25 subject from KVKR Face database this database is developed under UGC-SAP Phase I (our own major contribution) having 10 pose of each subject, 40 subjects from ORL having 10 images each, and from IIT Indian database 56 subjects, 11 images per subjects. After apply PCA on ORL Database the highest RR 97.50% for experiment 1:8 images, on KVKR-Face Database we got 92.00% RR for 1:9 and on IIT-Indian database 62.50% RR for 1:9. After applied LDA on ORL database we got the result 80.00% is highest RR, also applied on KVKR-Face database we got excellent result 100% RR and on IIT-Indian database 64.29% RR. Keywords. PCA, LDA, Biometrics, Face Recognition, Eigen Value, Euclidian Distance, Scatter Matrix.

1. Introduction

B. Face recognition

A. Biometrics

Human face is very popular biometrics due to its technique of identification of a person by their visual contact. Because of its prospective applications researchers, neuroscientist, engineers, etc are concentrated work on automatic face recognition system for access using computer vision. For any automatic face recognition process face detection is required; it is very challenging task because there variations in pose (i.e. Front- Non-Front), image occlusion, orientation, illumination, facial expression, etc. [5].

Biometrics is rapidly evolving technology or science of establishing the automatic identification of an individual based on the physical, chemical or behavioural attributes of the person [1,2]. These characteristics are referred as exclusive for each and every person on the earth called as biometrics identifiers. Biometrics can be classified in Behavioural [3] such as voice, signature, and the way of typing on keyboard are vary over a time, in Physical such as iris, retina patterns, geometric shape of your hand, facial features, fingerprint which does not alter throughout our life and in Chemical category DNA, Blood Group, odour, saliva, etc. are used. Biometrics is a technique in which we can work out for distinct features of individuals are analysed and stored as bio-prints [4] in a reference database on a smart card or an embedded chip and that are used to check for new bio-prints for verification of identity. 196

C. Face recognition process Face recognition process follow certain steps such as acquiring samples through sensors or camera, extract the features from the image by applying different techniques, compare the template with existing database for identification or verification and declare person is authenticate or not [7].

ELSEVIER PROCEEDING, 2ND NATIONAL CONFERENCE ON ADVANCEMENTS IN THE ERA OF MULTI-DISCIPLINARY SYSTEMS (AEMDS-2013), APRIL 6-7, 2013

Face Recognition using Principle Component Analysis and Linear Discriminant Analysis Comparative Study Table 1. KVKR face database. Properties # of subjects # of images/videos Static/Videos Single/Multiple faces Gray/Color Resolution Face pose

Facial expression Figure 1. Classification of face recognition method

2. Face Recognition History First time in the 1960s face recognition system comes to existence they had calculated features like mouth, eyes, nose and ears, etc from photograph manually and compare the distances with stored reference data. Goldstein, Harmon and Lesk in 1970 used some 21 definite features such as hair colour, lip thickness, etc. Both systems were manual computing to overcome this problem Kirby and Sirovich (1988) used PCA. In the 1991 for pure automatic system Turk and Pentland exposed eigenfaces [6] There are various algorithm are used for ImageBased Face Recognition as shown in Figure 1 such as PCA, ICA, LDA, EBGM, Kernel Methods, Trace Transform, AAM, 3-D Morphable Model, 3-D Face Recognition, Bayesian Framework, SVM, HMM, Boosting & Ensemble Algorithms Comparisons, etc. [8]. The current face recognition techniques can be classified into four main categories (Figure 1) based on the way these represent and identify the face: In this paper we used PCA and LDA for our experimental work and follow steps to implement the algorithm as per [30–32]. 3. Experiment & Result A. Experimental setup For the comparative study of face recognition technique several experiments has been done using two well-known appearance based face recognition algorithm (PCA and LDA) and three face databases (KVKR-Face Database, ORL and IIT Indian Database). 1. In First Step Enrol images from database one image of each subject.

Illumination Accessories 3D data Ground truth

Descriptions 25 250 Both Multiple Color 640*480 Normal, looking left, looking right with 45◦ , looking up, looking down Neutral, small smile, big smile, closed eye, N/A Glasses, beards, moustaches N/A N/A

2. In second step for testing image train database by taking images of each subject from individual database. At the time of selecting the images for testing we have keep the enrolimages as it is and increase the testing images by 1 of each subject for all databases. 3. Enrol images excluded from testing images. 4. Apply the PCA and LDA on both enrol as well as testing images. 5. For matching purpose Euclidian distance is calculated. 6. Same procedure is followed for taking result of all databases. B. Databases a. KVKR face database Source: This database is developed by KVKR (Prof. K. V. Kale Research Group www.kvkale.in) under UGC SAP (II) DRS Phase-I, in the Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (Maharashtra), India. Purpose: Multimodal Biometrics. b. Indian database Source: This database contains human face images captured in February, 2002 in the campus of Indian Institute of Technology Kanpur [13–16].


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AEMDS-2013 Table 2. Indian Database. Properties # of subjects # of images/videos Static/Videos Single/Multiple faces Gray/Color Resolution Face pose

Facial expression Illumination Accessories 3D data Ground truth

Descriptions 56 616 Static Single? Color 640*480 front, looking left, looking right, looking up, looking up towards left, looking up towards right, looking down neutral, smile, laughter, sad/disgust N/A beards, moustaches N/A N/A

Figure 2. Comparative FAR of PCA

c. ORL database Source: This database is constructed by AT & T Laboratories Cambridge. Purpose: This database is primarily used for face recognition [17–29]. Table 3. ORL Database. Properties

Descriptions

# of subjects # of images/videos Static/Videos Single/Multiple faces Gray/Color Resolution Face pose

40 400 Static Single Eight-bit gray 92*112 Moderate pose variation (up and down, quarter-profile to frontalview) 3 facial expressions: neutral, smiling, closed eye N/A Glasses N/A Cropped face region Identifications of subjects

Facial expression Illumination Accessories 3D data Ground truth

C. Evaluation terms 1. False acceptance rate The system in which system match with non– authenticate person. In this system person is not valid 198

Figure 3. Comparative FAR of LDA

but system consider as a genuine person [11] is: ⎛ ⎞ Number of importer transaction ⎜ ⎟ attempts accepted ⎟ − 100 FAR = ⎜ ⎝ ⎠ Total number of importer transaction attemps 2. Recognition rate (RR) The rate in which system matches the correct person as genuine with stored database is: ⎛ ⎞ Number of transaction ⎜ attempts true accepted ⎟ ⎟ − 100 RR = ⎜ ⎝ Total number of ⎠ transaction attemps D. Results E. Discussion 1. In the first experiment 1:1 evaluation is used i.e. one image for enrolment and 1 image for training of a same person, images taken from KVKR–Face Database, ORL Database, and Indian


Face Recognition using Principle Component Analysis and Linear Discriminant Analysis Comparative Study

Figure 4. Comparative recognition rate of PCA

Figure 5. Comparative recognition rate of LDA

Database separately. All technical specification of databases is given in the Table I-III, 25:25 images from KVKR-Face Database, 40:40 images from ORL and 56:56 images from IIT-Indian Database. We applied the Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on these databases and we got the result 65.00% Recognition Rate (RR) on ORL database by using LDA and using PCA got 72.50% RR. On other hand using KVKR-Face database with LDA we got 76.00% RR and with PCA we get 60.00% RR. With IIT-Indian database by using LDAand PCA we get 41.07% RR. 2. In a second experiment the same procedure was followed, we had increased training image size as two instead of one hence 1:2 is evaluated. For enrolment we keep images as it is and for training 50, 80,112 images taken from KVK, ORL and IIT respectively. We got the result for ORL 75.00% RR on LDA, 80.00% RR on PCA likewise for KVKRFace database by using LDA 92.00% RR, on PCA 80.00% RR and for IIT-Indian Database 44.64% RR, 42.86% RR respectively. As compare to earlier result ORL RR is increased by 10.00%, on

KVKR- Face database is 16.00%, for IIT-Indian Database 8.93% increased by using LDA and for PCA 7.50%, 20.00%, 1.79% respectively. For ORL and KVKR-Face database RR is increased because of less variation in the images. 3. In third experiments we followed same steps as mentioned in the above results. Here we just change the training images per person is three i.e.75, 120,168 images taken from KVK, ORL and IIT respectively. Here we got the result for RR 75.00%, 80.00% for ORL Database by using LDA & PCA technique, for KVKR-Face Database 92.00%, 84.00% respectively and for IIT Indian Database 53.57%, 51.79% correspondingly. In this experiment we didn’t find the changes in RR on the ORL database for LDA & PCA but in IIT-Indian database RR is increased by 8.93% in LDA as well as PCA because of some images are closely related to enrolment image. In PCA KVKR-Face database RR is increased 4.00%, LDA no change. 4. In fourth experiment we followed same procedure; here also we increase the training images as 100, 160, 224 for each database. In this experiment we got the 77.50% RR, 82.50% RR with LDA & PCA for ORL Database, 92.00%, 84.00% RR for KVKR-Face Database respectively, 57.14%, 51.79% RR for LDA and PCA simultaneously. In this experiment recognition rate is improved by 2.5% of PCA and LDA on ORL Database, there is no difference found in RR on KVKR-Face database, on IIT Indian database RR is improved by 3.57% because variation of pose is reduced due to the increase the number of samples for the training set. 5. In this experiment we followed the same procedure for training and testing. Training set is increased by five (1:5) images per subjects as 125, 200, 280 for KVK, ORL and IIT database correspondingly. We got the result RR is 80.00%, 95.00% for LDA & PCA on ORL Database, on KVKR-Face Database 92.00%, 88.00% RR accordingly, and on IITIndian database for LDA we got the RR is 57.14%, for PCA 48.21% RR. In this experiment on IIT-Indian database by using PCA RR is reduced −3.57% because here testing images having various poses as compare to enrol image, we select the minimum Euclidian distance between training image and testing image, due to the variation in the pose distance is increased of same subjects hence RR is decreased.


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6. In this experiment we had taken the one image for enrolment and six images for training of each subject from KVKR-Face, ORL, and IIT-Indian database as 150, 240, 336 for each. We apply LDA and PCA on these database and we got the result 80.00%, 95.00% RR on ORL, 100.00%, 88.00% RR on KVKR-Face, and on IIT-Indian 55.36%, 53.57% RR respectively. Here we got very good result for KVKR-Face database i.e.100% because we had created a database in a such a way that it will keep the variations but slightly changes in a pose therefore performance of this database is increased and hence gives best RR. But on IIT-Indian database exactly the reverse thing happened hence RR is decreased by −1.79% on LDA. 7. In this experiment followed same method as mentioned in the previous results. Here we had taken training images as 175, 280, 392 from each dataset such as KVK, ORL and IIT. We applied LDA and PCA on ORL database and got the 80.00%, 95.00% RR on both techniques respectively, also on KVKR-Face database RR is 100%, 88.00% and on IIT-Indian database 58.93%, 51.79%. We observed that on KVR-Face database RR is remains same but on IIT-Indian database RR is decrease by −1.79% using PCA because of as explained in previous experiments and RR is increased up to 3.57% because LDA gives clear cut classification of classes. 8. In this experiment we evaluated same steps as followed in previous experiment, training set is increased by one details i.e. 200, 320, 448 from each database. We applied LDA and PCA on ORL database and we got the 88.00% & 97.50% RR, on KVKR-Face database 100%, 88.00% RR, and on IIT-Indian database 60.71%, 58.93% RR respectively. Here we found that PCA RR is increased 2.50% on ORL database because of in this testing set most of images are comes with less variations as compare to previous one. 9. At the final experiment we had taken single image for enrolment and nine images for training purpose from the KVR-Face database, ORL database, and IIT-Indian database 225, 360, 504 respectively. We applied these techniques on databases and we got the result for ORL database 77.5%, 95.00% RR, also on the KVKR-Face database 96.00%,

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92.00% RR, and on IIT-Indian database 64.29%, 62.50% RR. As compare to previous result on IIT-Indian database RR is increased by 3.57% because in IITIndian database each subject having 11 different poses with 90◦ − 45◦ with various facial expressions. We are taking the Euclidian distance hence we got the improved results. But on ORL database RR is decreased by 2.50% because all variations are comes in the testing set therefore we get reduced RR. And on KVKR-Face database RR is decreased using LDA by 4.00% same reason like ORL database, and by using PCA RR is increased by 4.00% because variations are comes in the training set but it get only the principle value from the image therefore RR is improved. 4. Conclusion As we gone through the experimental results we found that Recognition Rate some time increased sometime decreased, sometime constant. We have done various experiments like single image for enrolment and single image for testing, then we had keep the enrolment image as constant and changetesting images such as two, three, four, up to nine. For taking the result we had used three databases ORL Database, KVKR-Face Database and IIT-Indian Database. When we applied PCA on ORL Database we got the highest RR 97.50% for experiment 1:8 images, on KVKR-Face Database we got 92.00% RR for 1:9 and on IIT-Indian database 62.50% for 1:9. RR is varying because of database; ORL database having image resolution is less as compare to other i.e. 112*92, Images are in grey color, variation frontal poses is less on other hand in KVKR-Face database and IIT-Indian database too many variation like head rotate 90◦ − 45◦ , with expression, with illumination, with beard. In ORL Database face are cropped but in KVKR-Face database and IIT-Indian database faces are not cropped therefore RR is high on ORL database as compare to other. When we applied LDA on ORL database we got the result 80.00% is highest RR, also applied on KVKRFace database we got excellent result 100% RR and on IIT-Indian database 64.29% RR. The reason behind the different RR is that LDA do the more separation among the classes, KVKR-Face database is color database hence no loss of data and there is less variations in the pose of subjects. But in


Face Recognition using Principle Component Analysis and Linear Discriminant Analysis Comparative Study

IIT-Indian database RR is less as compare to others because of the too many expressions, head pose is vary, male as well as female, with illuminations and more No. of images. Therefore it is conclude that face recognition rate is depend on the which database you are using, on which condition it is created, no of samples per subjects, variation in the poses and angles. And more important thing is which classifier is used to classify the faces. If algorithms gives more features it gives good results. In our result it is observed and concludes that LDA is most suitable for face recognition as compare to PCA. Future scope: Face recognition systems used in this work very well with some constrained, although all systems work much better with different pose. But algorithms can give less results when the face is partial, with different expressions, 2D, 3D database. Hence there is much scope to improve results with partial or 2D, 3D databases or different expressions. System will need to recognize people in real-time and in much less constrained situations. In future we can implement face recognition system with real time with less expensive. There is ample scope in surveillance for face recognition to attendance system. It may also use same techniques for Multimodal Biometrics. In Future it can be use more databases and check the performances of the algorithms. In future we can use different techniques and check reliability of KVKRFace Database. We may use same for identification of face recognition on video database. Acknowledgment We would like to acknowledge and extend our heartfelt gratitude to the UGC who have funded for development of UGC SAP (II) DRS Phase-I F.No-3-42/2009 major research project entitled “Biometrics: Multimodal System Development”. We also acknowledge to Professor and Head, Dr. K. V. Kale to give us permission to use “Biometrics Multimodal Research Lab” for development of database and research work in the Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad. We are also highly thankful to University Grants Commission [UGC], New Delhi for financial assistance and award of fellowships under “Rajiv Gandhi National Fellowship Scheme [RGNFS-JRF]”, “Maulana Azad National Fellowship Scheme for

Minority Students [MANFSMS-JRF]”, “UGC-BSR Meritorious Fellowship Scheme [BMFS-JRF]” for this research work.

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