Swarm Optmization Algorithms for Face Recognition

Ray, Ray (2013) Swarm Optmization Algorithms for Face Recognition. BTech thesis.



In this thesis, a face recognition system based on swarm intelligence is developed. Swarm intelligence can be defined as the collective intelligence that emerges from a group of simple entities; these agents enter into interactions, sense and change their environment locally. A typical system for face recognition consists of three stages: feature extraction, feature selection and classification. Two approaches are explored. First, Bacterial Foraging Optimization(BFO), in which the features extracted from Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA) are optimized. Second, Particle Swarm Optimization(PSO), which optimizes the transform coefficients obtained from the Discrete Cosine Transform(DCT) of the images. PCA, LDA and DCT are all appearance-based methods of feature extraction. PCA and LDA are based on global appearance whereas DCT is performed on a block by block basis exploring the local appearance-based features. Finally, for classification Euclidean distance metric is used. The algorithms that have been applied are tested on Yale Face Database.

Item Type:Thesis (BTech)
Uncontrolled Keywords:Swarm intelligence; feature extraction; feature selection; PCA; LDA; DCT; BFO; PSO
Subjects:Engineering and Technology > Computer and Information Science > Image Processing
Divisions: Engineering and Technology > Department of Computer Science
ID Code:5112
Deposited By:Hemanta Biswal
Deposited On:09 Dec 2013 09:07
Last Modified:09 Dec 2013 09:07
Supervisor(s):Majhi, B

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