A Real-Time Face Recognition System Using Eigenfaces
Keywords:
Feature vector, eigenfaces, eigenvalues, eigenvector, face recognition, real-timeAbstract
A real-time system for recognizing faces in a video stream provided by a surveillance camera was implemented, having real-time face detection. Thus, both face detection and face recognition techniques are summary presented, without skipping the important technical aspects. The proposed approach essentially was to implement and verify the algorithm Eigenfaces for Recognition, which solves the recognition problem for two dimensional representations of faces, using the principal component analysis. The snapshots, representing input images for the proposed system, are projected in to a face space (feature space) which best defines the variation for the face images training set. The face space is defined by the ‘eigenfaces’ which are the eigenvectors of the set of faces. These eigenfaces contribute in face reconstruction of a new face image projected onto face space with a meaningful (named weight).The projection of the new image in this feature space is then compared to the available projections of training set to identify the person using the Euclidian distance. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions.References
D. Maltoni, D. Maio, A.K. Jain, and S. Prabhakar, Handbook of Fingerprint recognition. Springer Verlag, New York, USA, 2003.
A. K. Jain, A. Ross, and S. Prabhakar, An Introduction to Biometric Recognition. IEEE Transactions on circuits and systems for video technology, Vol.14 No.1, January 2004.
D. M. Blackburn. Biometrics 101, version 3.1. Federal Bureau of Investigation, March 2004
J. F. Canny A Computational Approach to Edge Detection, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. Pami-8, No. 6, November 1986.
P. J. Phillips, P. Grother, R. J. Michaels, D. Blackburn, E. Tabassi, and M. Bone. Face Recognition Vendor Test 2002: Evaluation Report. Online: http://www.frvt.org, March 2003.
M.-H. Yang, D. J. Kriegman, and N. Ahuja, Detecting faces in images: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol. 24, No.1, pp. 34–58, 2002.
Stan Z. Li, Anil K. Jain, Handbook of Face Recognition, Springer Science+Business Media, LLC, New York, USA, 2005.
P. Viola, M. Jones, Rapid object detection using a boosted cascade of simple features, IEEE Conference on Computer Vision and Pattern Recognition, CVPR, Hawaii, 2001.
P. Viola, M. Jones, Robust real-time object detection, Second International Workshop on Statistical And Computational Theories Of Vision – Modelling, Learning Computing, And Sampling, Vancouver, 2001.
C. P. Papageorgiou, M. Oren, T. Poggio, A General Framework for Object Detection, International Conference on Computer Vision (ICCV), pp. 555-562, India, 1998.
M. A. Turk, A. P. Pentland, Face recognition using eigenfaces, IEEE Conference on Computer Vision and Pattern Recognition - CVPR, pp 586-591, 1991.
M. A. Turk and A. P. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, Massachusetts Institute of Technology, 1991.
I. Atalay, Face recognition using eigenfaces, M. Sc. Thesis, Technical University Institute of Science and Technology, pp -58-59, Istanbul, 1996
Ö. Toygar, A. Acan, Face Recognition Using PCA, LDA And ICA Approaches On Colored Images, Journal Of Electrical & Electronics Engineering, Vol.3, No.1, pp. 735-743, Turkey, 2003.
Face images databases
[IMG1] The Yale Face Database. Online: http://cvc.yale.edu/projects/yalefaces/yalefaces.html
http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html
[IMG2] Spacek L., “Description of Libor Spacek's Collection of Facial Images”, 1996, Online:
http://cswww.essex.ac.uk/mv/allfaces/index.html
[IMG3] Lena
http://www.ee.columbia.edu/~sfchang/course/dip/images/lena.jpg
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