TACIDS: Tactical Identification System Using Facial Recognition
Facial recognition system
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Transcript of Facial recognition system
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FACIAL RECOGNITION SYSTEM USING EIGEN FACES
Presented By: M.Divya Sushma
(08PA1A0433)
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Digital image processing is a rapidly evolving field with growing
applications in science and engineering . Image processing holds
the probability of developing the ultimate machine that could
perform the visual function of all living beings.
Here an approach is made to detect and identify a human
face and describe the algorithm for software implementation of
face recognition system using eigenface. In eigenface method,
training set is prepared first and then the person is recognized by
comparing characteristics of the face to those of known individuals.
ABSTRACT
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Face is our primary focous of interaction with society, face
communicates identify, Emotion, race and age. It is also quite
useful for judging gender, size and perhaps even character Of
the person.
INTRODUCTION:
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The major approaches used for face recognition are
1.Featured based approach
2.Eiganface based approach
APPROACHES FOR FACE RECOGNITION:
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1.Feature based approach:
First order features values
Second order features values
2. Eigen Face Based Approach:
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BLOCK DIAGRAM OF FACE RECOGNITION SYSTEM
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HISTORY:
HOW EIGEN FACES WILL GENERATED:
FACE RECOGNITION USING EIGENFACES
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EIGENFACE-BASED FACIAL RECOGNITION ALGORITHM
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Calculation of eigenfaces with PCAIn this section, the original scheme for determination of the eigenfaces using PCA will be presented. The algorithm described in scope of this paper is a variation of the one outlined here.
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Face Recognition Using Eigenfaces
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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
MERITS AND DEMERITS
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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.
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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.
APPLICATION
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SOME IMAGES OF FACE RECOGNITION
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EIGEN FACES
EIGENFACES RECONSTRUCTION
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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
CONCLUSION
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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).
REFERENCES:
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QUERIES ?
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THANK YOU