ResGait: gait feature refinement based on residual structure for gait recognition
Abstract Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gao, Shuo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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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| Übergeordnetes Werk: |
Enthalten in: The visual computer - Berlin : Springer, 1985, 39(2023), 8 vom: 26. Juni, Seite 3455-3466 |
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| Übergeordnetes Werk: |
volume:39 ; year:2023 ; number:8 ; day:26 ; month:06 ; pages:3455-3466 |
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| DOI / URN: |
10.1007/s00371-023-02973-0 |
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SPR052784398 |
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| 520 | |a Abstract Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. | ||
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| 650 | 4 | |a Soft threshold |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Residual structure |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Ning, Jingyu |4 aut | |
| 700 | 1 | |a Hou, Bingqian |4 aut | |
| 700 | 1 | |a Li, Li |4 aut | |
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10.1007/s00371-023-02973-0 doi (DE-627)SPR052784398 (SPR)s00371-023-02973-0-e DE-627 ger DE-627 rakwb eng Gao, Shuo verfasserin aut ResGait: gait feature refinement based on residual structure for gait recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. Biometrics (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Gait recognition (dpeaa)DE-He213 Soft threshold (dpeaa)DE-He213 Residual structure (dpeaa)DE-He213 Tan, Zhenhua (orcid)0000-0002-9870-8925 aut Ning, Jingyu aut Hou, Bingqian aut Li, Li aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 26. Juni, Seite 3455-3466 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:26 month:06 pages:3455-3466 https://dx.doi.org/10.1007/s00371-023-02973-0 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_267 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_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 39 2023 8 26 06 3455-3466 |
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10.1007/s00371-023-02973-0 doi (DE-627)SPR052784398 (SPR)s00371-023-02973-0-e DE-627 ger DE-627 rakwb eng Gao, Shuo verfasserin aut ResGait: gait feature refinement based on residual structure for gait recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. Biometrics (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Gait recognition (dpeaa)DE-He213 Soft threshold (dpeaa)DE-He213 Residual structure (dpeaa)DE-He213 Tan, Zhenhua (orcid)0000-0002-9870-8925 aut Ning, Jingyu aut Hou, Bingqian aut Li, Li aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 26. Juni, Seite 3455-3466 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:26 month:06 pages:3455-3466 https://dx.doi.org/10.1007/s00371-023-02973-0 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_267 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_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 39 2023 8 26 06 3455-3466 |
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10.1007/s00371-023-02973-0 doi (DE-627)SPR052784398 (SPR)s00371-023-02973-0-e DE-627 ger DE-627 rakwb eng Gao, Shuo verfasserin aut ResGait: gait feature refinement based on residual structure for gait recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. Biometrics (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Gait recognition (dpeaa)DE-He213 Soft threshold (dpeaa)DE-He213 Residual structure (dpeaa)DE-He213 Tan, Zhenhua (orcid)0000-0002-9870-8925 aut Ning, Jingyu aut Hou, Bingqian aut Li, Li aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 26. Juni, Seite 3455-3466 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:26 month:06 pages:3455-3466 https://dx.doi.org/10.1007/s00371-023-02973-0 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_267 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_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 39 2023 8 26 06 3455-3466 |
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10.1007/s00371-023-02973-0 doi (DE-627)SPR052784398 (SPR)s00371-023-02973-0-e DE-627 ger DE-627 rakwb eng Gao, Shuo verfasserin aut ResGait: gait feature refinement based on residual structure for gait recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. Biometrics (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Gait recognition (dpeaa)DE-He213 Soft threshold (dpeaa)DE-He213 Residual structure (dpeaa)DE-He213 Tan, Zhenhua (orcid)0000-0002-9870-8925 aut Ning, Jingyu aut Hou, Bingqian aut Li, Li aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 26. Juni, Seite 3455-3466 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:26 month:06 pages:3455-3466 https://dx.doi.org/10.1007/s00371-023-02973-0 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_267 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_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 39 2023 8 26 06 3455-3466 |
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10.1007/s00371-023-02973-0 doi (DE-627)SPR052784398 (SPR)s00371-023-02973-0-e DE-627 ger DE-627 rakwb eng Gao, Shuo verfasserin aut ResGait: gait feature refinement based on residual structure for gait recognition 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. Biometrics (dpeaa)DE-He213 Human identification (dpeaa)DE-He213 Gait recognition (dpeaa)DE-He213 Soft threshold (dpeaa)DE-He213 Residual structure (dpeaa)DE-He213 Tan, Zhenhua (orcid)0000-0002-9870-8925 aut Ning, Jingyu aut Hou, Bingqian aut Li, Li aut Enthalten in The visual computer Berlin : Springer, 1985 39(2023), 8 vom: 26. Juni, Seite 3455-3466 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:39 year:2023 number:8 day:26 month:06 pages:3455-3466 https://dx.doi.org/10.1007/s00371-023-02973-0 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_267 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_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 39 2023 8 26 06 3455-3466 |
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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 Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. 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resgait: gait feature refinement based on residual structure for gait recognition |
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ResGait: gait feature refinement based on residual structure for gait recognition |
| abstract |
Abstract Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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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Abstract Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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 Gait recognition is a biometric recognition technology, where the goal is to identify the subject by the subject’s walking posture at a distance. However, a lot of redundant information in gait sequence will affect the performance of gait recognition, and the most existing gait recognition models are overly complicated and parameterized, which leads to the low efficiency in model training. Consequently, how to reduce the complexity of the model and eliminate redundant information effectively in gait have become a challenging problem in the field of gait recognition. In this paper, we present a residual structure based gait recognition model, short for ResGait, to learn the most discriminative changes of gait patterns. To eliminate redundant information in gait, the soft thresholding is inserted into the deep architectures as a nonlinear transformation layer to improve gait feature learning capability from the noised gait feature map. Moreover, each sample owns unique set of thresholds, making the proposed model suitable for different gait sequences with different redundant information. Furthermore, residual link is introduced to reduce the learning difficulties and alleviate computational costs in model training. Here, we train the network in terms of various scenarios and walking conditions, and the effectiveness of the method is validated through abundant experiments with various types of redundant information in gait. In comparison to the previous state-of-the-art works, experimental results on the common datasets, CASIA-B and OUMVLP-Pose, show that ResGait has higher recognition accuracy under various walking conditions and scenarios. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 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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ResGait: gait feature refinement based on residual structure for gait recognition |
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Tan, Zhenhua Ning, Jingyu Hou, Bingqian Li, Li |
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10.1007/s00371-023-02973-0 |
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2024-07-03T14:46:32.996Z |
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| score |
7.3998127 |