Optimization in Face Recognition
Ant Colony Optimization For Feature Selection In Face Recognition
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Face Recognition, one of the most three most popular biometric (face, fingerprint and voice),always involves high dimensional data. As a consequence of increasing requirement due to its application in the government and commercial sector, the face recognition task has exhibited increased complexity and large storage capacity requirement. In order to reduce the complexity,it becomes essential to keep the dimensionality of the data as small as possible so as to make thesystem efficient as far as classification accuracy is concerned. The feature extraction/selection is done from the data to retai...
Face Recognition, one of the most three most popular biometric (face, fingerprint and voice),always involves high dimensional data. As a consequence of increasing requirement due to its application in the government and commercial sector, the face recognition task has exhibited increased complexity and large storage capacity requirement. In order to reduce the complexity,it becomes essential to keep the dimensionality of the data as small as possible so as to make thesystem efficient as far as classification accuracy is concerned. The feature extraction/selection is done from the data to retain most of the information that represents the original data.Before feature selection, we retained only thirty percent of total number of eigenvectorshas retained and the dimensionality was reduced. For the projection of the data, these retainedeigenvectors is to be used or not, are decided by the Ant colony optimization (ACO)algorithm.ACO algorithm is inspired of ant's social behavior in their search for the shortest pathsof food sources. In the feature selection algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO.
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