Title: A face recognition algorithm based on multiple convolution kernels and double layer sparse automatic encoder

Authors: Hao Wang; Xiuyou Wang; Huaming Liu; Dongqing Xu; Zhengyan Liu

Addresses: School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China ' School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China ' School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China ' School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China ' School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236037, China

Abstract: The prevailing face recognition algorithms adopt the manual design feature or the automatic extraction feature of deep learning in the process of characteristic extraction. In order to extract the distinguishing characteristics of the target more accurately, a face recognition algorithm based on the multiple convolution kernels and double-layer sparse automatic encoder was proposed. Initially, the proposed algorithm pre-treated the image with zero-phase component analysis (ZCA) whitening to decrease the correlation of the characteristic and reduced the complexity of the network training. Subsequently, a deep network characteristic extractor was designed, based on the convolution, pooling and multi-layer sparse automatic encoder. The convolution kernel was obtained by an independent unsupervised learning, and an automatic deep characteristic extractor was obtained by pre-training and fine-tuning. Finally, the extracted characteristics were classified using the Softmax regression model. The experiment results manifest that the presented algorithm was superior to the existing algorithms and conventional deep learning algorithms.

Keywords: face recognition; deep networks; multiple convolution kernels; double layer sparse automatic encoder.

DOI: 10.1504/IJSN.2020.111134

International Journal of Security and Networks, 2020 Vol.15 No.4, pp.224 - 232

Received: 30 Jan 2020
Accepted: 31 Jan 2020
Published online: 10 Nov 2020 *

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