A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features

Abstract 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 introd...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Huang, Zengxi [verfasserIn] Wang, Jie Wang, Xiaoming Song, Xiaoning Chen, Mingjin

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Biometric verification Sparse representation One-to-many matching Sparsity-based matching measures Multimodal biometrics Deep learning

Anmerkung:	© The Author(s) 2022

Übergeordnetes Werk:	Enthalten in: Complex & intelligent systems - Berlin : SpringerOpen, 2015, 9(2022), 2 vom: 22. Sept., Seite 1583-1603
Übergeordnetes Werk:	volume:9 ; year:2022 ; number:2 ; day:22 ; month:09 ; pages:1583-1603

Links:	Volltext

DOI / URN:	10.1007/s40747-022-00868-6

Katalog-ID:	SPR050100815

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520			\|a Abstract 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.
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allfields	10.1007/s40747-022-00868-6 doi (DE-627)SPR050100815 (SPR)s40747-022-00868-6-e DE-627 ger DE-627 rakwb eng Huang, Zengxi verfasserin aut A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract 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. Biometric verification (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 One-to-many matching (dpeaa)DE-He213 Sparsity-based matching measures (dpeaa)DE-He213 Multimodal biometrics (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Jie aut Wang, Xiaoming aut Song, Xiaoning aut Chen, Mingjin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 22. Sept., Seite 1583-1603 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:22 month:09 pages:1583-1603 https://dx.doi.org/10.1007/s40747-022-00868-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 22 09 1583-1603
spelling	10.1007/s40747-022-00868-6 doi (DE-627)SPR050100815 (SPR)s40747-022-00868-6-e DE-627 ger DE-627 rakwb eng Huang, Zengxi verfasserin aut A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract 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. Biometric verification (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 One-to-many matching (dpeaa)DE-He213 Sparsity-based matching measures (dpeaa)DE-He213 Multimodal biometrics (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Jie aut Wang, Xiaoming aut Song, Xiaoning aut Chen, Mingjin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 22. Sept., Seite 1583-1603 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:22 month:09 pages:1583-1603 https://dx.doi.org/10.1007/s40747-022-00868-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 22 09 1583-1603
allfields_unstemmed	10.1007/s40747-022-00868-6 doi (DE-627)SPR050100815 (SPR)s40747-022-00868-6-e DE-627 ger DE-627 rakwb eng Huang, Zengxi verfasserin aut A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract 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. Biometric verification (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 One-to-many matching (dpeaa)DE-He213 Sparsity-based matching measures (dpeaa)DE-He213 Multimodal biometrics (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Jie aut Wang, Xiaoming aut Song, Xiaoning aut Chen, Mingjin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 22. Sept., Seite 1583-1603 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:22 month:09 pages:1583-1603 https://dx.doi.org/10.1007/s40747-022-00868-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 22 09 1583-1603
allfieldsGer	10.1007/s40747-022-00868-6 doi (DE-627)SPR050100815 (SPR)s40747-022-00868-6-e DE-627 ger DE-627 rakwb eng Huang, Zengxi verfasserin aut A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract 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. Biometric verification (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 One-to-many matching (dpeaa)DE-He213 Sparsity-based matching measures (dpeaa)DE-He213 Multimodal biometrics (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Jie aut Wang, Xiaoming aut Song, Xiaoning aut Chen, Mingjin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 22. Sept., Seite 1583-1603 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:22 month:09 pages:1583-1603 https://dx.doi.org/10.1007/s40747-022-00868-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 22 09 1583-1603
allfieldsSound	10.1007/s40747-022-00868-6 doi (DE-627)SPR050100815 (SPR)s40747-022-00868-6-e DE-627 ger DE-627 rakwb eng Huang, Zengxi verfasserin aut A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract 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. Biometric verification (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 One-to-many matching (dpeaa)DE-He213 Sparsity-based matching measures (dpeaa)DE-He213 Multimodal biometrics (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wang, Jie aut Wang, Xiaoming aut Song, Xiaoning aut Chen, Mingjin aut Enthalten in Complex & intelligent systems Berlin : SpringerOpen, 2015 9(2022), 2 vom: 22. Sept., Seite 1583-1603 (DE-627)835589269 (DE-600)2834740-7 2198-6053 nnns volume:9 year:2022 number:2 day:22 month:09 pages:1583-1603 https://dx.doi.org/10.1007/s40747-022-00868-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022 2 22 09 1583-1603
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source	Enthalten in Complex & intelligent systems 9(2022), 2 vom: 22. Sept., Seite 1583-1603 volume:9 year:2022 number:2 day:22 month:09 pages:1583-1603
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author	Huang, Zengxi
spellingShingle	Huang, Zengxi misc Biometric verification misc Sparse representation misc One-to-many matching misc Sparsity-based matching measures misc Multimodal biometrics misc Deep learning A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features
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topic_title	A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features Biometric verification (dpeaa)DE-He213 Sparse representation (dpeaa)DE-He213 One-to-many matching (dpeaa)DE-He213 Sparsity-based matching measures (dpeaa)DE-He213 Multimodal biometrics (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213
topic	misc Biometric verification misc Sparse representation misc One-to-many matching misc Sparsity-based matching measures misc Multimodal biometrics misc Deep learning
topic_unstemmed	misc Biometric verification misc Sparse representation misc One-to-many matching misc Sparsity-based matching measures misc Multimodal biometrics misc Deep learning
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title	A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features
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title_full	A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features
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title_sort	study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features
title_auth	A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features
abstract	Abstract 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. © The Author(s) 2022
abstractGer	Abstract 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. © The Author(s) 2022
abstract_unstemmed	Abstract 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. © The Author(s) 2022
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title_short	A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features
url	https://dx.doi.org/10.1007/s40747-022-00868-6
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A study of sparse representation-based classification for biometric verification based on both handcrafted and deep learning features

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