Title: Multi-pose face recognition method based on improved depth residual network

Authors: Feigang Tan; Yi Tang; Jiaojun Yi

Addresses: School of Traffic and Environment, Shen Zhen Institute of Information Technology, Shenzhen, 518172, China ' School of Information Technology and Engineering, Guangzhou College of Commerce, GuangZhou, 511363, China ' School of Economics, Guangzhou College of Commerce, Guangzhou, 511363, China

Abstract: Multi-pose face recognition method can reduce the interference of pose change on face characteristics by analysing pose change. In order to improve the accuracy of multi-pose face recognition and shorten the recognition time, a multi-pose face recognition method based on improved depth residual network is proposed. The multi-pose face image is transformed logarithmically, and the face image is enhanced by homomorphic filtering algorithm. The spatial transformation network is introduced to improve the depth residual network model, and the enhanced face image is input into the improved depth residual network model. Through the calculation of loss function and the update of gradient parameters, the multi-pose face image recognition is completed. The experimental results show that this method has strong multi-pose face image enhancement ability, can effectively recognise multi-pose face images, and has high recognition accuracy. When the occlusion is 30%, the face recognition accuracy can reach 0.989.

Keywords: improved depth residual network; multi-pose; face recognition; image enhancement; Softmax regression model.

DOI: 10.1504/IJBM.2024.140780

International Journal of Biometrics, 2024 Vol.16 No.5, pp.514 - 532

Received: 01 Aug 2023
Accepted: 19 Oct 2023
Published online: 02 Sep 2024 *

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