Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Dynamic Vision: From Images to Face Recognition
Dynamic Vision: From Images to Face Recognition
Journal of Global Optimization
Bacteria Foraging Based Independent Component Analysis
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Automatic image pixel clustering with an improved differential evolution
Applied Soft Computing
A novel adaptive bilinear filter based on pipelined architecture
Digital Signal Processing
Automatic circle detection on digital images with an adaptive bacterial foraging algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Image Classification Using Correlation Tensor Analysis
IEEE Transactions on Image Processing
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The present paper describes a novel method of implementation of a stochastic optimization technique for the face recognition problem. The method proposed divides the original images into patches in space, and seeks a non-linear functional mapping using second-order Volterra kernels. The artificial bee colony optimization technique, a modern stochastic optimization algorithm, is used to derive optimal Volterra kernels during training to simultaneously maximize inter-class distances and minimize intra-class distances in the feature space. During testing, a voting procedure is used in conjunction with a nearest neighbor classifier to decide to which class each individual patch belongs. Finally, the aggregate classification results of all patches in an image are used to determine the overall recognition outcome for the given image. The utility of the proposed scheme is aptly demonstrated by implementing it on two popular benchmark face recognition datasets, and comparing the effectiveness of the proposed approach vis-a-vis other statistical learning procedures in facial recognition and also several other methods developed so far. The effectiveness of the artificial bee colony optimization technique and its Levy-mutated variation in optimizing Volterra kernels is conclusively proven in this paper by significantly outperforming many popular contemporary algorithms.