Title: A local occlusion face recognition method based on improved deep residual network

Authors: Yan Lei; Xinhua Wang; Zitong Wang

Addresses: Software College, Shangqiu Polytechnic, Shangqiu, 476000, China ' Department of Criminal Science and Technology, Henan Police College, Zhengzhou, 450046, China ' City College, Dalian University of Technology, Dalian, 116600, China

Abstract: In order to improve the accuracy of local occlusion face recognition, a local occlusion face recognition method based on an improved deep residual network is proposed. Firstly, by utilising collaborative sparse measurement and compressive sensing techniques combined with local 2D sparse and non-local 3D sparse transformations, image denoising and restoration can be achieved. Secondly, the greyscale morphology theory is used to perform edge detection on locally occluded facial images. By segmenting image regions and calculating appropriate thresholds for each region, the edge feature information of the facial image is accurately extracted and recognised. Finally, an optimised sRSNet-I model was designed for local occlusion face recognition, utilising neural network layers for feature extraction and classification to achieve local occlusion face recognition. The experimental results show that the recognition accuracy of our method consistently maintains a high level of over 96%, and the recognition time remains between 0.54 and 0.62 seconds.

Keywords: improved deep residual network; partial occlusion; facial recognition; edge feature information.

DOI: 10.1504/IJBM.2026.156234

International Journal of Biometrics, 2026 Vol.18 No.5, pp.484 - 500

Received: 17 Mar 2025
Accepted: 17 Jul 2025
Published online: 08 Sep 2026 *

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