Title: Feature extraction method of face image texture spectrum based on a deep learning algorithm

Authors: Suhua Wang; Zhiqiang Ma; Xiaoxin Sun

Addresses: College of Science and Technology, Humanities and Sciences of Northeast Normal University, China ' College of Science and Technology, Humanities and Sciences of Northeast Normal University, China ' College of Information Science and Technology, Northeast Normal University, China

Abstract: Deep learning has made great progress in the field of face recognition, but most of the current face feature matching algorithms focus on the matching of a single image and another single image, and can not effectively use the relevant information between image sequences, in order to avoid the influence of human factors on the skin texture feature extraction of face image. In this paper, a texture spectrum feature extraction method based on deep learning is proposed. The face image is extracted by CNN network, and the similar image sequences are automatically selected for feature matching by using the improved sparse expression method to obtain the relevant information between the face image sequences. The experimental results show that the algorithm has achieved good results in LFW and AR databases and is superior to the traditional SRC, L1 norm and CRC-RLS algorithms.

Keywords: face image texture spectrum feature; constrained sparse representation; deep learning; image sequence; feature extraction; simulation.

DOI: 10.1504/IJBM.2021.114649

International Journal of Biometrics, 2021 Vol.13 No.2/3, pp.195 - 210

Received: 21 Mar 2020
Accepted: 15 Jun 2020
Published online: 29 Apr 2021 *

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