Digital image processing is a rapidly evolving field with growingapplications in science and engineering . Image processing holds theprobability of developing the ultimate machine that could performthe visual function of all living beings. Here an approach is made to detect and identify a humanface and describe the algorithm for software implementation of facerecognition system using eigenface. In eigenface method, trainingset is prepared first and then the person is recognized by comparingcharacteristics of the face to those of known individuals.
Face is our primary focous of interaction with society, facecommunicates identify, Emotion, race and age. It is also quiteuseful for judging gender, size and perhaps even character Ofthe person.
The major approaches used for face recognition are1.Featured based approach2.Eiganface based approach
1.Feature based approach: First order features values Second order features values2. Eigen Face Based Approach:
In this section, the original scheme for determination of theeigenfaces using PCA will be presented. The algorithmdescribed in scope of this paper is a variation of the oneoutlined here.
MERITS: Complete face information is taken into account for recognition. Relative insensitivity to small or gradual change in the face image. Better in speed , simplicity and learning capability
DEMERITS: If lighting effects and the position of the face with respect to the camera is varied Greately then accuracy will effect. Only gray scale images can be detected A noisy image or partially occluded face causes recognition performance to degrade gracefully.
Face recognition system has following application: Given a database of standard face images (say criminal mug shots), determine whether or not a new shot of a person is in database. Authorize users to allow login access. Prepare a surveillance camera system residing at some public place which automatically matches the input faces with criminal database and gives alert if the results are matched. Match the person with his passport image, licence image etc.
Face Recognition has been successfully implemented using eigenface approach. Eigenface approach of face recognition has been found to be a robust technique that can be used in security systems
T. M. Mitchell. Machine Learning. McGraw-Hill International Editions, 1997. D. Pissarenko. Neural networks for financial time series prediction: Overview over recent research. BSc thesis, 2002. L. I. Smith. A tutorial on principal components analysis, February 2002. URL http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf. (URL accessed on November 27, 2002). M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3 (1), 1991a. URL http://www.cs.ucsb.edu/ mturk/Papers/jcn.pdf. (URL accessed on November 27, 2002). M. A. Turk and A. P. Pentland. Face recognition using eigenfaces. In Proc. of Computer Vision and Pattern Recognition, pages 586-591. IEEE, June 1991b. URLhttp://www.cs.wisc.edu/ dyer/cs540/handouts/mturk-CVPR91.pdf. (URL accessed on November 27, 2002).
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