Recognizing faces with normalized local Gabor features and Spiking Neuron Patterns (original) (raw)
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Recent Advancements in Gabor Wavelet-Based Face Recognition
Due to their biological relevance (Daugman, 1980; Marcelja, 1980) and computational properties Gabor wavelets were introduced to image analysis. As a feature generator Gabor filters are widely used in face recognition. . Since the kernels of Gabor wavelets are similar to the 2D receptive field profiles of the mammalian cortical simple cells, they exhibit desirable characteristics of spatial locality and orientation selectivity. Also they are optimally localized in the space and frequency domains. The Gabor wavelets (kernels / filters) can be defined as following, (Lades, et al., 1993)...
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In this paper, a two–level supervised feature selection algorithm for local feature–based face recognition is presented. In the first part, a genetic algorithm is used to determine the useful locations of the face region for recognition. 2D Gabor wavelet–based feature extractors are used for local image descriptors at these locations. In the second part, the most useful frequencies and orientations of Gabor kernels are determined using a floating feature selection algorithm.