Abstract
Similarity measure has long played a critical role and attracted great interest in various areas such as pattern recognition and machine perception. Nevertheless, there remains the issue of developing an efficient two-dimensional (2D) robust similarity measure method for images. Inspired by the properties of subspace, we develop an effective 2D image similarity measure technique, named transformation similarity measure (TSM), for robust face recognition. Specifically, the TSM method robustly determines the similarity between two well-aligned frontal facial images while weakening interference in the face recognition by linear transformation and singular value decomposition. We present the mathematical features and some odds to reveal the feasible and robust measure mechanism of TSM. The performance of the TSM method, combined with the nearest neighbor rule, is evaluated in face recognition under different challenges. Experimental results clearly show the advantages of the TSM method in terms of accuracy and robustness.
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Project supported by the National Natural Science Foundation of China (No. 61873106), the Natural Science Foundation of Jiangsu Province, China (No. BK20171264), the Jiangsu Qing Lan Project to Cultivate Middle-Aged and Young Science Leaders, China, the Jiangsu Six Talent Peak Project, China (Nos. XYDXX-047 and XYDXX-140), the University Science Research General Research General Project of Jiangsu Province, China (Nos. 18KJB520005 and 19KJB520004), the Innovation Fund Project for Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, China (No. JYB201609), the Lianyungang Hai Yan Plan, China (Nos. 2018-ZD-003, 2018-QD-001, and 2018-QD-012), the Science and Technology Project of Lianyungang Hightech Zone, China (Nos. ZD201910 and ZD201912), and the Natural Science Foundation Project of Huaihai Institute of Technology China (No. Z2017005)
Contributors
Jiang ZHANG and Heng ZHANG developed the idea of this study. Jian ZHANG designed the research. Jian ZHANG, Li-ling BO, Hong-ran LI, and Shuai XU processed the data. Jian ZHANG drafted the manuscript. Heng ZHANG and Dong-qing Yuan helped organize the manuscript. Jian ZHANG and Heng ZHANG revised and finalized the paper.
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Jian ZHANG, Heng ZHANG, Li-ling BO, Hong-ran LI, Shuai XU, and Dong-qing YUAN declare that they have no conflict of interest.
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Zhang, J., Zhang, H., Bo, Ll. et al. Subspace transform induced robust similarity measure for facial images. Front Inform Technol Electron Eng 21, 1334–1345 (2020). https://doi.org/10.1631/FITEE.1900552
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DOI: https://doi.org/10.1631/FITEE.1900552