11 January 2022 Research on recognition of painted faces
Tian Dai, Xiaoli Ma, Jiayin Dai, Mengya Qi, Guanghao Zhu, Jie Yuan
Author Affiliations +
Abstract

In the field of public safety surveillance, suspects may disguise themselves by painting their faces, challenging the face recognition system. The well-developed ordinary, two-dimensional image face recognition methods rely mainly on the texture features of the face images, which leaves the traditional face recognition system relatively vulnerable to the camouflage attacks. We set our sights on three-dimensional (3D) alternatives. A 3D facial recognition method is proposed which extracts 3D features of each individual that are not related to face texture. Considering actual requirements, we applied binocular stereo matching to obtain 3D face point clouds. Hereafter, we included a spatial representation for classification and recognition of 3D face, based on which we then adopted a multicascade classifier. By reconstructing, extracting features, and identifying multiple 3D faces, comparison experiment results demonstrate that our proposed method discerned 3D faces correctly, defeated the face camouflage attacks effectively, and showed promising application prospects for public safety surveillance.

© 2022 SPIE and IS&T 1017-9909/2022/$28.00 © 2022 SPIE and IS&T
Tian Dai, Xiaoli Ma, Jiayin Dai, Mengya Qi, Guanghao Zhu, and Jie Yuan "Research on recognition of painted faces," Journal of Electronic Imaging 31(1), 013005 (11 January 2022). https://doi.org/10.1117/1.JEI.31.1.013005
Received: 11 August 2021; Accepted: 21 December 2021; Published: 11 January 2022
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CITATIONS
Cited by 1 scholarly publication.
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KEYWORDS
Facial recognition systems

3D modeling

3D image processing

Nose

3D image reconstruction

Feature extraction

Clouds

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