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Syst."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Biometric verification is generally considered a one-to-one matching task. In contrast, in this paper, we argue that the one-to-many competitive matching via sparse representation-based classification (SRC) can bring enhanced verification security and accuracy. SRC-based verification introduces non-target subjects to construct dynamic dictionary together with the client claimed and encodes the submitted feature. Owing to the sparsity constraint, a client can only be accepted when it defeats almost all non-target classes and wins a convincing sparsity-based matching score. This will make the verification more secure than those using one-to-one matching. However, intense competition may also lead to extremely inferior genuine scores when data degeneration occurs. Motivated by the latent benefits and concerns, we study SRC-based verification using two sparsity-based matching measures, three biometric modalities (i.e., face, palmprint, and ear) and their multimodal combinations based on both handcrafted and deep learning features. We finally approach a comprehensive study of SRC-based verification, including its methodology, characteristics, merits, challenges and the directions to resolve. Extensive experimental results demonstrate the superiority of SRC-based verification, especially when using multimodal fusion and advanced deep learning features. The concerns about its efficiency in large-scale user applications can be readily solved using a simple dictionary shrinkage strategy based on cluster analysis and random selection of non-target subjects.<\/jats:p>","DOI":"10.1007\/s40747-022-00868-6","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T07:03:00Z","timestamp":1663830180000},"page":"1583-1603","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features"],"prefix":"10.1007","volume":"9","author":[{"given":"Zengxi","family":"Huang","sequence":"first","affiliation":[]},{"given":"Jie","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaoming","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaoning","family":"Song","sequence":"additional","affiliation":[]},{"given":"Mingjin","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"868_CR1","volume-title":"Handbook of biometrics","author":"AK Jain","year":"2007","unstructured":"Jain AK, Flynn P, Ross AA (2007) Handbook of biometrics. 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