A STUDY ON FACE RECOGNITION
TECHNIQUE BASED ON EIGENFACE
By:
Sadique Nayeem
Pondicherry University
Outline
Overview
Eigenface Algorithm
Implementation
Image Database
Experimental Result
Future Enhancement
Conclusions
2
Overview
Face recognition system consist of three component.
 Face Representation: How to model a face?
 Template-based ...
Eigenfaces Approach
 In the language of information theory…
 the main objective is to mine the relevant information in a...
Eigenface Initialization
The eigenfaces approach for face recognition involves the following
initialization operations:
 ...
Eigenface Recognition
Having initialized the system, the following steps are used to recognize
new face images:
 Given an...
Image Database
Name
of
database
Source Image
format
Image
size
Image
type
Number
of unique
individual
Total
numbe
rof
imag...
Experimental Result
8
Eigenface face recognition with different sample images
Name
of
databas
e
Total
No. of
unique
person...
Experimental Result (cont..)
9
Number of samples
Future Enhancement
 According to the experimental result, recognition with one sample
per person does not give better rec...
Problem Statement
 This problem can be defined as follows:
“Given a stored database of faces with only one image per pers...
Proposed Idea
 1.2 billion population of India is being enrolled for ADHAAR Card with
different biometric.
 Face image i...
Proposed Idea (contd.)
 To restrain the crime, ADHAAR Card can be the best source for
identification.
 Individual images...
Conclusions
 Eigenface PCA is one of the most successful technique and it gives
better result for more number of samples ...
Reference
1. “Eigenfaces for recognition”, M. Turk and A. Pentland, Jo urnalo f Co g nitive
Ne uro scie nce , vo l. 3, No ...
Thank You !
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A study on face recognition technique based on eigenface

  1. 1. A STUDY ON FACE RECOGNITION TECHNIQUE BASED ON EIGENFACE By: Sadique Nayeem Pondicherry University
  2. 2. Outline Overview Eigenface Algorithm Implementation Image Database Experimental Result Future Enhancement Conclusions 2
  3. 3. Overview Face recognition system consist of three component.  Face Representation: How to model a face?  Template-based approaches  Feature-based approaches  Appearance-based approaches  Face Detection: To locate a face in image.  Manipulation of images in “face space”  Utilization of elliptical shape of human head  Face Identification: Compare given image with database.  Performance affected by scale, pose, illumination, facial expression, and disguise, etc. 3
  4. 4. Eigenfaces Approach  In the language of information theory…  the main objective is to mine the relevant information in a face image, encode it as efficiently as possible and compare one face encoding with a database of face images encoded in the same process.  In mathematical terms…  Find the principal components of the face distribution, or the eigenvectors of the covariance matrix of the set of face images, called e ig e nface s.  Eigenfaces are a set of features that characterize the variation between face images. Each training face image can be represented in terms of a linear combination of the eigenfaces, so can the new input image.  Compare the feature weights of the new input image with those of the known individuals 4
  5. 5. Eigenface Initialization The eigenfaces approach for face recognition involves the following initialization operations:  Acquire a set of training images.  Calculate the eigenfaces from the training set, keeping only the best M images with the highest eigenvalues. These M images define the “face space”. As new faces are experienced, the eigenfaces can be updated.  Calculate the corresponding distribution in M-dimensional weight space for each known individual (training image), by projecting their face images onto the face space. 5
  6. 6. Eigenface Recognition Having initialized the system, the following steps are used to recognize new face images:  Given an image to be recognized, calculate a set of weights of the Meigenfaces by projecting it onto each of the eigenfaces.  Determine if the image is a face at all by checking to see if the image is sufficiently close to the face space.  If it is a face, classify the weight pattern as either a known person or as unknown. 6 Figure : Eigenfaces of Essex face database -'face94'
  7. 7. Image Database Name of database Source Image format Image size Image type Number of unique individual Total numbe rof images Variations Sample Image IFD IIT Kanpur [3] JPG 110 X 75 Color 60 660 8 pose, 3 emotion Essex face databas e -face94 University of Essex, UK [4] JPG 90 X 100 Color 152 3040 facial expression, slight head tilt. Yale Yale university [5] GIF 320 X 243 Grey 15 165 facial expression, w/o glasses Face 1999 California Institute of Technolo gy [6] JPG 300X198 Color 26 450 lighting, expression, background 7
  8. 8. Experimental Result 8 Eigenface face recognition with different sample images Name of databas e Total No. of unique person No. of samples of each image in training set No. of image in training set No. of False recognition Accuracy rate (%) IFD 60 1 60 31 49.18 2 120 25 59.01 3 180 16 73.77 4 240 16 73.77 5 300 12 80.32 6 360 8 86.88 7 420 3 95.08 8 480 2 96.72 9 540 1 98.36 10 600 1 98.36 11 660 1 98.36 Esse x face 152 1 152 47 69.07 2 304 29 80.92 3 456 12 92.10 4 608 11 92.76 5 760 11 92.76 6 912 10 93.42 7 1064 10 93.42 8 1216 9 94.07 9 1368 8 94.73 10 1520 8 94.73 11 1672 6 96.05 Yale 15 1 15 8 46.66 2 30 2 86.66 3 45 3 80.00 4 60 3 80.00 5 75 2 86.66 6 90 1 93.33 7 105 2 86.66 8 120 1 93.33 9 135 1 93.33 10 150 1 93.33 11 165 1 93.33 Face 1999 26 1 26 17 34.61 2 52 15 42.30 3 78 14 46.15 4 104 9 65.38 5 130 9 65.38 6 156 8 69.23 7 182 5 80.76 8 208 5 80.76 9 234 3 88.46 10 260 2 92.30 11 286 1 96.15 Eigenface face recognition with different sample images Name of databas e Total No. of unique person No. of samples of each image in training set No. of image in training set No. of False recognition Accuracy rate (%)
  9. 9. Experimental Result (cont..) 9 Number of samples
  10. 10. Future Enhancement  According to the experimental result, recognition with one sample per person does not give better recognition rate in all cases.  But, in real time application only one sample per person will be available ( as in case of voter card, Driving license, passport or ADHAAR Card).  So, recognition from single sample per person is needed.  One sample per person is easy to collect, save storage cost and save computational cost. 10 Courtesy: http://images.google.co.in/
  11. 11. Problem Statement  This problem can be defined as follows: “Given a stored database of faces with only one image per person, the goal is to identify a person from the database later in time in any different and unpredictable poses, lighting, disguise, etc from the individual image.” 11
  12. 12. Proposed Idea  1.2 billion population of India is being enrolled for ADHAAR Card with different biometric.  Face image is also being collected.  The ADHAAR Card or UID no. can be used as a platform on which different application can be developed as under: 12 ADHAAR CARD or UID NUMBER
  13. 13. Proposed Idea (contd.)  To restrain the crime, ADHAAR Card can be the best source for identification.  Individual images in ADHAAR Card may work as training set.  CCTV images from crime scene can be used as test image.  Procedure:  Capture the video from the CCTV camera.  Detect the human face in the CCTV video.  Take the CCTV image as the test image.  Do the preprocessing on the CCTV image i.e  Crop both the eyes, eyebrow, nose, and mouth.  Load the ADHAAR based Face image as the training image  Crop both the eyes, eyebrow, nose, and mouth  Apply the Eigenface PCA for the Recognition 13
  14. 14. Conclusions  Eigenface PCA is one of the most successful technique and it gives better result for more number of samples in training set.  It does not produce good result for single sample per person.  The need for real time application can be given by only single sample per person.  Taking ADHAAR Card as a platform, Artificial Face Recognition system can be developed by using PCA on reconstructed image. 14
  15. 15. Reference 1. “Eigenfaces for recognition”, M. Turk and A. Pentland, Jo urnalo f Co g nitive Ne uro scie nce , vo l. 3, No . 1 , 1 9 9 1 2. “Automatic recognition and analysis of human faces and facial expressions: A survey”, A. Samal and P. A. Iyengar, Patte rn Re co g nitio n, 25(1 ): 6 5-7 7 , 1 9 9 2 3. “The Indian Face Database”, Vidit Jain, Amitabha Mukherjee, 2002, http://vis- www. cs. um ass. e du/~ vidit/IndianFace Database / 4. “Essex face database -face94”, University of Essex, UK, http: //cswww. e sse x. ac. uk/m v/allface s/inde x. htm l 5. “Yale Database”, http: //cvc. yale . e du/pro je cts/yale face s/yale face s. htm l 6. “FACE 1999”, http: //www. visio n. calte ch. e du/htm l-file s/archive . htm l 15
  16. 16. Thank You ! 16

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