Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks

Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplis...
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Autor*in:	Srivastava, Swati [verfasserIn] Sharma, Himanshu

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Supervised Unsupervised Face recognition Human identification Complex domain Neural network Fuzzy clustering Evolutionary computation

Anmerkung:	© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 83(2023), 5 vom: 12. Juli, Seite 14085-14109
Übergeordnetes Werk:	volume:83 ; year:2023 ; number:5 ; day:12 ; month:07 ; pages:14085-14109

Links:	Volltext

DOI / URN:	10.1007/s11042-023-16274-0

Katalog-ID:	SPR054536308

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520			\|a Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods.
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allfields	10.1007/s11042-023-16274-0 doi (DE-627)SPR054536308 (SPR)s11042-023-16274-0-e DE-627 ger DE-627 rakwb eng Srivastava, Swati verfasserin aut Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods. Supervised (dpeaa)DE-He213 Unsupervised (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Complex domain (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Sharma, Himanshu aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 5 vom: 12. Juli, Seite 14085-14109 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:5 day:12 month:07 pages:14085-14109 https://dx.doi.org/10.1007/s11042-023-16274-0 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 5 12 07 14085-14109
spelling	10.1007/s11042-023-16274-0 doi (DE-627)SPR054536308 (SPR)s11042-023-16274-0-e DE-627 ger DE-627 rakwb eng Srivastava, Swati verfasserin aut Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods. Supervised (dpeaa)DE-He213 Unsupervised (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Complex domain (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Sharma, Himanshu aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 5 vom: 12. Juli, Seite 14085-14109 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:5 day:12 month:07 pages:14085-14109 https://dx.doi.org/10.1007/s11042-023-16274-0 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 5 12 07 14085-14109
allfields_unstemmed	10.1007/s11042-023-16274-0 doi (DE-627)SPR054536308 (SPR)s11042-023-16274-0-e DE-627 ger DE-627 rakwb eng Srivastava, Swati verfasserin aut Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods. Supervised (dpeaa)DE-He213 Unsupervised (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Complex domain (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Sharma, Himanshu aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 5 vom: 12. Juli, Seite 14085-14109 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:5 day:12 month:07 pages:14085-14109 https://dx.doi.org/10.1007/s11042-023-16274-0 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 5 12 07 14085-14109
allfieldsGer	10.1007/s11042-023-16274-0 doi (DE-627)SPR054536308 (SPR)s11042-023-16274-0-e DE-627 ger DE-627 rakwb eng Srivastava, Swati verfasserin aut Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods. Supervised (dpeaa)DE-He213 Unsupervised (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Complex domain (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Sharma, Himanshu aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 5 vom: 12. Juli, Seite 14085-14109 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:5 day:12 month:07 pages:14085-14109 https://dx.doi.org/10.1007/s11042-023-16274-0 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 5 12 07 14085-14109
allfieldsSound	10.1007/s11042-023-16274-0 doi (DE-627)SPR054536308 (SPR)s11042-023-16274-0-e DE-627 ger DE-627 rakwb eng Srivastava, Swati verfasserin aut Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods. Supervised (dpeaa)DE-He213 Unsupervised (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Complex domain (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213 Sharma, Himanshu aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 83(2023), 5 vom: 12. Juli, Seite 14085-14109 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:83 year:2023 number:5 day:12 month:07 pages:14085-14109 https://dx.doi.org/10.1007/s11042-023-16274-0 lizenzpflichtig 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_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 83 2023 5 12 07 14085-14109
language	English
source	Enthalten in Multimedia tools and applications 83(2023), 5 vom: 12. Juli, Seite 14085-14109 volume:83 year:2023 number:5 day:12 month:07 pages:14085-14109
sourceStr	Enthalten in Multimedia tools and applications 83(2023), 5 vom: 12. Juli, Seite 14085-14109 volume:83 year:2023 number:5 day:12 month:07 pages:14085-14109
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Supervised Unsupervised Face recognition Human identification Complex domain Neural network Fuzzy clustering Evolutionary computation
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container_title	Multimedia tools and applications
authorswithroles_txt_mv	Srivastava, Swati @@aut@@ Sharma, Himanshu @@aut@@
publishDateDaySort_date	2023-07-12T00:00:00Z
hierarchy_top_id	27135030X
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author	Srivastava, Swati
spellingShingle	Srivastava, Swati misc Supervised misc Unsupervised misc Face recognition misc Human identification misc Complex domain misc Neural network misc Fuzzy clustering misc Evolutionary computation Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks
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topic_title	Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks Supervised (dpeaa)DE-He213 Unsupervised (dpeaa)DE-He213 Face recognition (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Complex domain (dpeaa)DE-He213 Neural network (dpeaa)DE-He213 Fuzzy clustering (dpeaa)DE-He213 Evolutionary computation (dpeaa)DE-He213
topic	misc Supervised misc Unsupervised misc Face recognition misc Human identification misc Complex domain misc Neural network misc Fuzzy clustering misc Evolutionary computation
topic_unstemmed	misc Supervised misc Unsupervised misc Face recognition misc Human identification misc Complex domain misc Neural network misc Fuzzy clustering misc Evolutionary computation
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title	Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks
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title_full	Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks
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author_browse	Srivastava, Swati Sharma, Himanshu
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title_sort	face recognition for human identification through integration of complex domain unsupervised and supervised frameworks
title_auth	Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks
abstract	Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. These results clearly demonstrate the superior performance of our approach, showcasing the dominance of the combined unsupervised and supervised frameworks over other state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Face recognition for human identification through integration of complex domain unsupervised and supervised frameworks
url	https://dx.doi.org/10.1007/s11042-023-16274-0
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Human identification can be performed through various available biometric traits such as the face, iris, fingerprint, ECG, gait, and ear. Among them, face is one of the most popular and widely used biometrics. In the security domain, early warnings and the trace of suspects can be accomplished using face recognition. The contemplated augmentation projected an intelligent computational model for human recognition which is an ingenious melding of unsupervised outline and complex domain neurocomputing. The unsupervised framework of our proposal constitutes evolutionary fuzzy computations in complex domain. The supervised schema capitalizes on a complex domain neural network with higher-order neurons and resilient propagation algorithm. Trainable multiple stages are populated in this proposal for the estimation of recognition and classification. This proposal offers an intelligent performance on recognition and classification tasks. Comprehensive experimental analysis on the datasets of AR face, PubFig83, and Indian face evidenced the enhanced precision of the proposed model. Our model achieves an impressive accuracy range of 97% to 99% across all datasets. 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