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Study and Analysis of Novel Face Recognition Techniques using PCA, LDA and Genetic Algorithm
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Study and Analysis of Novel Face Recognition Techniques using PCA, LDA and Genetic Algorithm

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Study and Analysis of Novel Face Recognition Techniques using PCA, LDA and Genetic Algorithm

Study and Analysis of Novel Face Recognition Techniques using PCA, LDA and Genetic Algorithm

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Study and Analysis of Novel Face Recognition Techniques using PCA, LDA and Genetic Algorithm Presentation Transcript

  • 1. STUDY AND ANALYSIS OF NOVEL FACE RECOGNITION TECHNIQUES USING PCA, LDA AND GENETIC ALGORITHM By: Sadique Nayeem Pondicherry University
  • 2. Outline Overview Image Database PCA & LDA Experimental Result Proposed Method Implementation Experimental Result Conclusions 2
  • 3. Overview  The face plays a major role in our social interaction in conveying identity and emotion.  Face recognition by human is quite robust, despite large changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses or changes in hairstyle.  Developing a computational model of face recognition is quite difficult, because faces are complex, multidimensional, and subject to change over time.  In the last two decade, a number of face recognition technique has been developed, but they lack in robustness and they work well for specific face databases. 3
  • 4. Image Database Name of databas e Source Image format Image size Imag e type Number of unique individua l Total numb er of image s Variations Sample Image IFD IIT Kanpur JPEG 110 X 75 Color 60 660 8 pose, 3 emotion Essex face databas e - face94 University of Essex, UK JPEG 90 X 100 Color 152 3040 facial expression, slight head tilt. Yale Yale university GIF 320 X 243 Gray 15 165 facial expression, w/o glasses Face 1999 California Institute of Technolo gy JPEG 300 X 198 Color 26 450 lighting, expression, Background UMIST University JPEG 92 X 112 Gray 20 564 Vary pose 4
  • 5. PRINCIPLE COMPONENT ANALYSIS RESULT 5 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 IFD Face94 Yale Face 1999 UMIST Number of samples RecognitionAccuracy(%) NUMBER OF INDIVIDUALS: 273 NUMBER OF IMAGES USED : 18018 Fig. 1 Result of PCA
  • 6. LINEAR DISRIMINANT ANALYSIS RESULT 6 0 10 20 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 IFD Face94 Yale Face 1999 UMIST NUMBER OF INDIVIDUALS: 273 NUMBER OF IMAGES USED : 18018 RecognitionAccuracy(%) Number of samples Fig. 2 Result of LDA
  • 7. PROPOSED METHOD 7
  • 8. Genetic Algorithm Applied to Face Recognition A method for face recognition by genetic algorithm has been proposed. First of all, a set of training images and testing images are given STEPS: 1. Convert all the images of the training set into gray scale then into column vector as shown in the figure below: 8 Fig. 3 Converting training set image into column vector
  • 9. 2. Select the image (to be tested) from the testing set, convert the image into gray scale then into column vector as shown in the figure below: 3. For more than one sample per person apply crossover operator to produce more number of images per person otherwise go to step 4. 9 a b c d 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 I. 0 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 II. 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 III. Genetic Algorithm Applied to Face Recognition Fig. 4 Converting testing image into column vector
  • 10. Genetic Algorithm Applied to Face Recognition 4. For one sample per person apply mutation at the least significant bits of chromosome. 5. Determine the fitness function value by using the Euclidian distance between the test image and the training set images. 10 a b Fig. 5 Mutation applied to image vector
  • 11. Genetic Algorithm Applied to Face Recognition 6. If any individual obtain a value of the fitness function below the threshold one, the system recognizes the image same as the test image, otherwise. 7. Increase the generation count. Go to step 3 and repeat step 3 to 8 till the counter has reached a maximum number generation T (defined by the user). 11
  • 12. EXPERIMENTAL RESULTS OF GENETIC ALGORITHM APPLIED TO FACE RECOGNITION 12
  • 13. Selection of Training Set and Testing set 13 Fig. 6 Selecting training database Fig. 7 Selecting training database
  • 14. Selection of Test Image & Output 14 Fig. 8 Input the test image. Fig. 9 Test image as the input Fig. 10 Equivalent image as the output
  • 15. Result at Generation: 0 15 20 30 40 50 60 70 80 90 100 1 2 3 4 5 IFD Face94 Yale Face 1999 UMIST Generation: 0 Number of samples RecognitionAccuracy(%) Fig. 11 Result at Generation 0
  • 16. Result at Generation: 1 16 Generation: 1 20 30 40 50 60 70 80 90 100 1 2 3 4 5 IFD Face94 Yale Face 1999 UMIST Number of samples RecognitionAccuracy(%) Fig. 12 Result at Generation 1
  • 17. Result at Generation: 2 17 20 30 40 50 60 70 80 90 100 1 2 3 4 5 IFD Face94 Yale Face 1999 UMIST Generation: 2 Number of samples RecognitionAccuracy(%) Fig. 13 Result at Generation 2
  • 18. Result at Generation: 3 18 20 30 40 50 60 70 80 90 100 1 2 3 4 5 IFD Face94 Yale Face 1999 UMIST Generation: 3 Number of samples RecognitionAccuracy(%) Fig. 14 Result at Generation 3
  • 19. Result at Generation: 4 19 20 30 40 50 60 70 80 90 100 1 2 3 4 5 IFD Face94 Yale Face 1999 UMIST Generation: 4 Number of samples RecognitionAccuracy(%) Fig. 15 Result at Generation 4
  • 20. Conclusions  PCA and LDA technique for face recognition fails for one image per person but gives good result for around 10 image per person.  Collection, storage and computation of 10 images per person for face recognition system is not possible.  Genetic algorithm provides good result for one image per person and instead of 10 images per person in PCA and LDA, Genetic algorithm gives almost same result with 5 images per person.  Thus application of genetic algorithm reduces the problems of collection and storage of images and computation complexity of the face recognition system.  In future different classifier can be used in place of PCA. 20
  • 21. Publication  “A Study on Face Recognition Technique based on Eigenface”, Dr. S. Ravi, Sadique Nayeem, International Journal of Applied Information Systems (IJAIS), Foundation of Computer Science FCS, New York, USA Volume 5– No.4, March 2013.  “Face Recognition using PCA and LDA: Analysis and Comparison”, Dr. S. Ravi, Sadique Nayeem. Uploaded in “International Conference on Advances in Recent Technologies in Communication & Computing 2013”, to be organized by ACEEE. 21
  • 22. Reference 1. “Eigenfaces for recognition”, M. Turk and A. Pentland, Journal of Cognitive Neuroscience, vol.3, No.1, 1991 2. “Automatic recognition and analysis of human faces and facial expressions: A survey”, A. Samal and P. A. Iyengar, Pattern Recognition, 25(1): 65-77, 1992 3. “Using Discriminant Eigenfeatures for Image Retrieval”, D.L.Swets and J. Weng, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8 August 1996. 4. “The Indian Face Database”, Vidit Jain, Amitabha Mukherjee, 2002, http://vis- www.cs.umass.edu/~vidit/IndianFaceDatabase/ 5. “Essex face database -face94”, University of Essex, UK, http://cswww.essex.ac.uk/mv/allfaces/index.html 6. “Yale Database”, http://cvc.yale.edu/projects/yalefaces/yalefaces.html 7. “FACE 1999”, http://www.vision.caltech.edu/html-files/archive.html 8. UMIST Face Database, http://www.sheffield.ac.uk/eee/research/iel/research/face 9. “Handbook of Face Recognition”, Stan Z. Li. and Anil K. Zain, Springer. 22
  • 23. Thank You ! 23