TAXONOMY OF NATURE INSPIRED ALGORITHM FOR FACE RECOGNITION

Abstract

Face Recognition is one of the crucial areas of research because of its widespread application. It is also a real world problem that can be solved with the help of computational intelligence techniques . Now, days area of computational intelligence is gaining a lot of interest. Many of the nature –inspired methodologies and approaches comes under this area are used to solve the real-world problem to which traditional approaches are infeasible, ineffective and less efficient. Computational intelligence primarily includes artificial neural networks, evolutionary computation, swarm intelligence and fuzzy logic. Face Recognition is a two step process i.e. features extraction and recognition process. In the feature extraction phase Gabor kernel is used to smoothen the images and PCA is used for feature extraction. Later on an evolutionary algorithm is used to find optimal features and an evolutionary algorithm is used to recognize an input image. We worked on both the phases to increase efficiency, by applying various evolutionary techniques. There are so many techniques available to solve the problem that the application developer gets into dilemma that which technique is most appropriate to use. In our thesis we have tried to solve the problem of face recognition using some of the evolutionary techniques like Ant Colony Optimization(ACO), Particle Swarm Optimization(PSO) ,Hybrid ACO/PSO, Biogeographical Based Optimization(BBO), Extended BBO and holistic technique like Principal Component Analysis(PCA). We analyzed and compared the results of all of these techniques to make it clear that which one is appropriate. Performance analysis is performed using Olivetti research Laboratory (ORL) face database and Cohn-Kanade database. We have shown the performance on the basis of time taken for recognition and accuracy in recognition. We found that different technique are best on both the parameters but if we are trying to find the appropriate one, Extended BBO serves the purpose.