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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-07-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1177/17483026251360208 |
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| Summary: | 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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| ISSN: | 1748-3026 |