12-12-2012, 01:58 PM
Application of Soft Computing to Face Recognition
Application of Soft Computing.pptx (Size: 2.02 MB / Downloads: 47)
Introduction
Face recognition is one of the most important abilities that we use in our daily lives.
Research in automatic face recognition started in the 1960s.
Because of the nature of the problem, not only computer science researchers are interested in it, but also neuroscientists and psychologists.
It is widely believed that one can instantly recognise thousands of people with whom one is familiar. As with many perceptual abilities, the ease with which humans can recognise faces disguises the complexity of the task.
Why Face Recognition?
Security
Fight terrorism
Find fugitives
Personal information access
ATM
Sporting events
Home access (no keys or passwords)
Any other application that would want personal identification
Improved human-machine interaction
Personalized advertising
Beauty search
Face Recognition System Requirements
Want the system to be inexpensive enough to use at many locations.
Match almost instantaneously
Before the person walks away from the advertisement
Before the fugitive has a chance to run away
Ability to handle a large database
Ability to do recognition in varying environments
Problem Statement
Given still or video images of a scene, identify one or more persons in the scene using a stored database of faces, or/and with available collateral information such as race, age and gender may be used in narrowing the search.
ANNs in Real Face Recognition
Many architectures are available but MLP is popular with back propagation algorithm.
Disadvantages: Complex and difficult to train
Difficult to implement
Sensitive to lighting variation
Model-Based Schemes
Changes in shape and changes in texture pattern across the face. Both shape and texture can also vary because of differences between individual and also due to changes in expression, lighting, viewpoint variations. There exists a strong concept known as model based approaches (statistical models of appearance),
The approach relies on a large and representative training set of facial images.
A feature-based system, based on elastic bunch graph matching, was developed by Wiskott et al.[12] .
2D demorphable face model used through which the face variations are learned [14],