Individual Recognition System using Deep network based on Face Regions

Abstract

Biometric based face recognition is a successful method for automatically identifying a person using her face, with a high confidence. For that reason, this paper introduces an efficient method for face recognition based on deep networks. It considers the three face regions: eye, mouth, and face. First, we have built one sparse autoencoder for every single region their outputs will be concatenated together and fed into another sparse autoencoder. After that, the softmax layer has been employed in the classification step. However, with a deep network method known as the softmax layer has been formed by stacking the encoders from the autoencoder. Followed by formed the full deep network. Finally, the results have been generated on the test set based on the deep network.  In the experimental stage, the Yale B database and the AR database and JAFFE database have been used to test the proposed individual recognition system. Experimental findings have clearly proven that the performance of the introduced algorithm is very encouraging and can respond to the security requirements.

Keywords

• Deep network,sparse autoencoder,hybrid face regions,individual recognition system,face recognition

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