Recovery-Based Occluded Face Recognition by Identity-Guided Inpainting
Author:
Li Honglei12ORCID, Zhang Yifan1, Wang Wenmin1ORCID, Zhang Shenyong1ORCID, Zhang Shixiong1
Affiliation:
1. School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China 2. Chongqing College of Electronic Engineering, Chongqing 401331, China
Abstract
Occlusion in facial photos poses a significant challenge for machine detection and recognition. Consequently, occluded face recognition for camera-captured images has emerged as a prominent and widely discussed topic in computer vision. The present standard face recognition methods have achieved remarkable performance in unoccluded face recognition but performed poorly when directly applied to occluded face datasets. The main reason lies in the absence of identity cues caused by occlusions. Therefore, a direct idea of recovering the occluded areas through an inpainting model has been proposed. However, existing inpainting models based on an encoder-decoder structure are limited in preserving inherent identity information. To solve the problem, we propose ID-Inpainter, an identity-guided face inpainting model, which preserves the identity information to the greatest extent through a more accurate identity sampling strategy and a GAN-like fusing network. We conduct recognition experiments on the occluded face photographs from the LFW, CFP-FP, and AgeDB-30 datasets, and the results indicate that our method achieves state-of-the-art performance in identity-preserving inpainting, and dramatically improves the accuracy of normal recognizers in occluded face recognition.
Funder
Science and Technology Development Fund (FDCT) of Macau
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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