A competitive-collaborative nonnegative representation method and its application for face recognition in smart campus
The nonnegative representation-based classification (NRC) method has attracted increasing attention in the field of face recognition. Building upon collaborative representation (CR), NRC incorporates a nonnegative constraint on the representation coefficients, thereby reducing the contribution of ir...
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| Veröffentlicht in: | Journal of algorithms & computational technology Jg. 19 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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SAGE Publishing
01.07.2025
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| ISSN: | 1748-3018, 1748-3026 |
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| Abstract | The nonnegative representation-based classification (NRC) method has attracted increasing attention in the field of face recognition. Building upon collaborative representation (CR), NRC incorporates a nonnegative constraint on the representation coefficients, thereby reducing the contribution of irrelevant training samples and enhancing overall classification performance. Despite these improvements, NRC inherits the same decision-making mechanism as the CR method, resulting in a decoupling of the representation and classification stages. This separation limits the method’s classification effectiveness. Furthermore, the presence of multicollinearity in the nonnegative representation may introduce inaccuracies in classification estimates, further undermining performance. To address these limitations, this paper proposes the competitive-collaborative nonnegative representation (CCNR) model. CCNR integrates two regularization terms: A competitive constraint and a collaborative constraint. The competitive constraint adopts a residual-based strategy during the classification stage, thereby strengthening the connection between representation and classification. This approach enables training samples from different classes to compete in representing the query sample, significantly improving classification performance. In parallel, the collaborative constraint applies an ℓ 2 -norm regularization to the representation coefficients, enhancing the stability of the model’s solution. Moreover, the CCNR model has been effectively deployed in smart campus environments. Extensive comparative experiments conducted on publicly available face datasets validate the effectiveness of the proposed model, consistently demonstrating its competitive performance. Habitually, the source code will be made available on the author’s profile page at https://github.com/li-zi-qi/CCNR . |
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| AbstractList | The nonnegative representation-based classification (NRC) method has attracted increasing attention in the field of face recognition. Building upon collaborative representation (CR), NRC incorporates a nonnegative constraint on the representation coefficients, thereby reducing the contribution of irrelevant training samples and enhancing overall classification performance. Despite these improvements, NRC inherits the same decision-making mechanism as the CR method, resulting in a decoupling of the representation and classification stages. This separation limits the method’s classification effectiveness. Furthermore, the presence of multicollinearity in the nonnegative representation may introduce inaccuracies in classification estimates, further undermining performance. To address these limitations, this paper proposes the competitive-collaborative nonnegative representation (CCNR) model. CCNR integrates two regularization terms: A competitive constraint and a collaborative constraint. The competitive constraint adopts a residual-based strategy during the classification stage, thereby strengthening the connection between representation and classification. This approach enables training samples from different classes to compete in representing the query sample, significantly improving classification performance. In parallel, the collaborative constraint applies an ℓ 2 -norm regularization to the representation coefficients, enhancing the stability of the model’s solution. Moreover, the CCNR model has been effectively deployed in smart campus environments. Extensive comparative experiments conducted on publicly available face datasets validate the effectiveness of the proposed model, consistently demonstrating its competitive performance. Habitually, the source code will be made available on the author’s profile page at https://github.com/li-zi-qi/CCNR . |
| Author | Sun, Jun Zhang, Yonghong Li, Ziqi Ren, Ke Guo, Tingting Xia, Qingfeng |
| Author_xml | – sequence: 1 givenname: Tingting surname: Guo fullname: Guo, Tingting organization: Information Construction and Management Centre, Wuxi University, Wuxi, China – sequence: 2 givenname: Ziqi orcidid: 0000-0003-4694-4456 surname: Li fullname: Li, Ziqi organization: School of Automation, Wuxi University, Wuxi, China, Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, China – sequence: 3 givenname: Jun surname: Sun fullname: Sun, Jun organization: Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, China, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China – sequence: 4 givenname: Yonghong surname: Zhang fullname: Zhang, Yonghong organization: School of Automation, Wuxi University, Wuxi, China – sequence: 5 givenname: Qingfeng surname: Xia fullname: Xia, Qingfeng organization: Information Construction and Management Centre, Wuxi University, Wuxi, China, School of Automation, Wuxi University, Wuxi, China – sequence: 6 givenname: Ke surname: Ren fullname: Ren, Ke organization: Information Construction and Management Centre, Wuxi University, Wuxi, China |
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| Cites_doi | 10.1109/TCYB.2020.3021712 10.1038/44565 10.3390/math12010052 10.1109/TPAMI.2008.79 10.1023/A:1022627411411 10.1016/j.neucom.2011.08.018 10.1177/17483026211065375 10.1177/17483026211044922 10.1109/CVPR.2016.322 10.1109/LGRS.2023.3282310 10.1109/TPAMI.2005.92 10.1007/s10489-021-02486-0 10.1109/TIP.2023.3322593 10.1016/j.patcog.2018.12.023 10.1561/2200000016 10.1016/j.neucom.2017.09.022 10.1109/TPAMI.2013.236 10.1109/TPAMI.2023.3279378 |
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