A study on face recognition technique based on eigenface

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A Study on Face Recognition Technique Based on Eigenface

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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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