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CONTENT BASED FACE RECOGNITION

CONTENT BASED FACE RECOGNITION. Under the guidance of Prof. Pushpak Bhattacharya . Ankur Jain 01D05007 Pranshu Sharma 01005026 Prashant Baronia 01D05005 Swapnil Zarekar 01D05001. Introduction. Problem Statement :

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CONTENT BASED FACE RECOGNITION

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  1. CONTENT BASED FACE RECOGNITION Under the guidance of Prof. Pushpak Bhattacharya Ankur Jain 01D05007 Pranshu Sharma 01005026 Prashant Baronia 01D05005 Swapnil Zarekar 01D05001

  2. Introduction Problem Statement : • Given an image, to identify it as a face and/or extract face images from it. • To retrieve the similar images (based on a heuristic) from the given database of face images.

  3. Why face recognition ? Various potential applications, such as • person identification. • human-computer interaction. • security systems.

  4. Difference From Image Recognition • Faces are complex, multidimensional and meaningful visual stimuli. • Face Recognition is difficult. • Face Images are similar in overall configuration.

  5. Approach • Similar toContent BasedImage Retrieval (CBIR). • Neural Networks andSelf Organizing Maps(SOMs). • Principal Component Analysis(PCA). • Relevancefeed back.

  6. Stages of Face Recognition • (1) face location detection • (2) feature extraction • (3) facial image classification • Approaches of Feature Extraction • (1) local feature : eyes, nose, mouth information • easily affected by irrelevant information . • (2) global feature : • extract feature from whole image .

  7. Face Recognition Using Eigenfaces

  8. Eigen Space and Eigen Faces • Face Images are projected into a feature space (“Face Space”) that best encodes the variation among known face images. • The face space is defined by the “eigenfaces”, which are the eigenvectors of the set of faces.

  9. Steps In Face Recognition • Initialization : • Acquire the training set and calculate eigenfaces (usingPCA projections) which define eigenspace. • When anew faceis encountered, calculate itsweight. • Determine if the image is face. • If yes,classify the weight patternas known or unknown. • (Learning) If the same unknown face is seen several timesincorporate it into knownfaces.

  10. PCA Main assumption of PCA approach: • Face spaceforms a clusterin image space. • PCA givessuitable representation.

  11. Eigenfaces (1) • Calculation of Eigenfaces (1) Calculate average face : v. (2) Collect difference between training images and average face in matrix A (M by N), where M is the number of pixels and N is the number of images. (3) The eigenvectors of covariance matrix C (M by M) give the eigenfaces. • M is usually big, so this process would be time consuming. What to do?

  12. Eigenfaces (2) • Calculation of Eigenvectors of C If the number of data points is smaller than the dimension (N<M), then there will be only N-1 meaningful eigenvectors. Instead of directly calculating the eigenvectors of C, we can calculate the eigenvalues and the corresponding eigenvectors of a much smaller matrix L (N by N). if λi are the eigenvectors of L then A λi are the eigenvectors for C. • The eigenvectors are in the descent order of the corresponding eigenvalues.

  13. Eigenfaces (3) • Representation of Face Images using Eigenfaces • The training face images and new face images can be represented as linear combination of the eigenfaces. • When we have a face image u : Since the eigenvectors are orthogonal :

  14. Eigenfaces (4) • Experiment and Results Data used here are from the ORL database of faces. Facial images of 16 personseach with 10 views are used. - Training set contains 16×7 images. - Test set contains 16×3 images. First three eigenfaces :

  15. Classification UsingNearest Neighbor • Save average coefficients for each person. Classifynew face as the person with the closest average. • Recognition accuracyincreases with number of eigenfaces till 15. Later eigenfacesdo not help muchwith recognition. Best recognition rates Training set 99% Test set 89%

  16. Neural Networks and TS-SOM

  17. What are Neural Networks ? • Individual units to simulate Neurons • Parallel Processing • Many inputs and single output • Organization/structure of the TLU’s is important

  18. What is SOM ? • TS-SOM :- Tree structureself-organizing maps • Competitive learning ANN • Each unit of map receives identicalinputs • Units compete for selection • Modification of selected node and its neighbors

  19. Training of SOM • Randomly initialized • Selection based on some query parameter • On selection a node and its neighbors are modified • Degree of modification reduces with each iteration

  20. Example of a two-dimensional TS-SOM structure of 3 levels

  21. Algorithm • Calculateweight vectorfor first level. • Initializeweight vectors of other levels. • Calculatecentroid associated to each node as mean of closest training samples. • Iterateto the next level.

  22. Relevance Feedback • Systemcontent basedretrieval. • Point of human intervention • User analysis of system output. • User selectsmost relevant • Query iteratedif output not satisfactory

  23. InteractionBetween User & System • A random set of faces is presented to the user. • User interactive selection of faces. • Systemcontent-based face retrieval. • User analysis of retrieved faces. • Requested face was found -> Exit • Similar faces were found. -> Go to 2 • No similar faces were found. • User tired -> Exit • User not tired (re initialization -> Go to 1

  24. Comparison of the Two Approaches • Training time Nearest neighbor is much faster. • Storage About the same. • Classification time Nearest neighbor is slightly slower. • Accuracy Neural network is able to achieve the same accuracy using 5 eigenfaces with nearest neighbor using 15, and a higher accuracy when using 15. Neural network models the problem better, but takes more training time.

  25. Future Work • Face Detection in motion pictures. • Detailed study of the proposed systemassuming PCA assumptions not to be true. • Investigate whethereigenfacesis a good solution for this problem by comparing with other feature extraction techniques such asDCT

  26. References • Navarrete P. and Ruiz-del-Solar J. (2002), “Interactive Face Retrieval using Self-Organizing Maps”, 2002 Int. Joint Conf. on Neural Networks – IJCNN 2002, May 12-17, Honolulu, USA. • “A tutorial on Principal Components Analysis”, By Lindsay I Smith. • “Eigenfaces for Recognition”, Turk, M. and Pentland A., (1991)Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86. • Ruiz-del-Solar, J., and Navarrete, P. (2002). “Towards a Generalized Eigenspace-based Face Recognition Framework”, 4th Int. Workshop on Statistical Techniques in Pattern Recognition, August 6-9, Windsor, Canada. • Simulating Neural Networks by James A. Freeman. • Artificial Intelligence by Neil J. Nielsson.

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