Feature map sharing hypercolumn model for shift invariant face recognition

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In this article, we propose a shift-invariant pattern recognition mechanism using a feature-sharing hypercolumn model (FSHCM). To improve the recognition rate and to reduce the memory requirements of the hypercolumn model (HCM), a shared map is constructed to replace a set of local neighborhood maps in the feature extraction and feature integration layers. The shared maps increase the ability of the network to deal with translation and distortion variations in the input image. The proposed face recognition system employed a FSHCM neural network to perform feature extraction and use a linear support vector machine for a recognition task. The effectiveness of the proposed approach is verified by measuring the recognition accuracy using the misaligned ORL face database.