Seeking the optimal accuracy-rate equilibrium in face recognition
Considerable facial data is often compressed prior to analysis to accommodate limitations in transmission or storage capacities. However, this compression may lead to the loss of crucial identity details, thereby diminishing the effectiveness of facial recognition (FR) systems. In this study, we aim...
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| Vydané v: | Neurocomputing (Amsterdam) Ročník 624; s. 129423 |
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Elsevier B.V
01.04.2025
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| ISSN: | 0925-2312 |
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| Abstract | Considerable facial data is often compressed prior to analysis to accommodate limitations in transmission or storage capacities. However, this compression may lead to the loss of crucial identity details, thereby diminishing the effectiveness of facial recognition (FR) systems. In this study, we aim to establish an optimal accuracy-rate equilibrium (ARE) to maximize the compression ratio without substantially compromising the performance of the FR system. We first investigate the effect of image compression in deep FR. Based on the definition of ARE in FR, we proposed a method to locate the ARE values of face images with true acceptance rate and false accept rate. Subsequently, we develop an ARE prediction method for the FR system (ARE-FR), which automatically infers ARE images of face images. The goal of the proposed ARE-FR is to maximize redundancy removal without impairment of robust identity information. Considering that high-level semantic features effectively capture crucial identity information, we force the proposed ARE-FR to focus only on the features in identity-related regions when predicting the ARE images. These features are derived from the interactive relationships between deep and shallow features. Experimental results have demonstrated that combining our proposed ARE-FR with the image coding algorithm is capable of saving more bits while maintaining the performance of the FR system. |
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| AbstractList | Considerable facial data is often compressed prior to analysis to accommodate limitations in transmission or storage capacities. However, this compression may lead to the loss of crucial identity details, thereby diminishing the effectiveness of facial recognition (FR) systems. In this study, we aim to establish an optimal accuracy-rate equilibrium (ARE) to maximize the compression ratio without substantially compromising the performance of the FR system. We first investigate the effect of image compression in deep FR. Based on the definition of ARE in FR, we proposed a method to locate the ARE values of face images with true acceptance rate and false accept rate. Subsequently, we develop an ARE prediction method for the FR system (ARE-FR), which automatically infers ARE images of face images. The goal of the proposed ARE-FR is to maximize redundancy removal without impairment of robust identity information. Considering that high-level semantic features effectively capture crucial identity information, we force the proposed ARE-FR to focus only on the features in identity-related regions when predicting the ARE images. These features are derived from the interactive relationships between deep and shallow features. Experimental results have demonstrated that combining our proposed ARE-FR with the image coding algorithm is capable of saving more bits while maintaining the performance of the FR system. |
| ArticleNumber | 129423 |
| Author | Wang, Shiqi Chen, Baoliang Tian, Yu Ou, Fu-Zhao Kwong, Sam |
| Author_xml | – sequence: 1 givenname: Yu surname: Tian fullname: Tian, Yu email: ytian73-c@my.cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong, China – sequence: 2 givenname: Fu-Zhao orcidid: 0000-0003-1245-8345 surname: Ou fullname: Ou, Fu-Zhao email: fuzhao.ou@my.cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong, China – sequence: 3 givenname: Shiqi surname: Wang fullname: Wang, Shiqi email: shiqwang@cityu.edu.hk organization: Department of Computer Science, City University of Hong Kong, Hong Kong, China – sequence: 4 givenname: Baoliang orcidid: 0000-0003-4884-6956 surname: Chen fullname: Chen, Baoliang email: blchen@scnu.edu.cn organization: Department of Computer Science, South China Normal University, China – sequence: 5 givenname: Sam orcidid: 0000-0001-7484-7261 surname: Kwong fullname: Kwong, Sam email: samkwong@ln.edu.hk organization: School of Data Science, Lingnan University, Hong Kong, China |
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