Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild

Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sun, Bo [verfasserIn] Li, Liandong Wu, Xuewen Zuo, Tian Chen, Ying Zhou, Guoyan He, Jun Zhu, Xiaoming

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Emotion recognition Multimodal features Feature-level fusion Decision-level fusion Multiple kernel learning Hierarchical classifier

Anmerkung:	© OpenInterface Association 2015

Übergeordnetes Werk:	Enthalten in: Journal on multimodal user interfaces - Berlin : Springer, 2007, 10(2015), 2 vom: 18. Nov., Seite 125-137
Übergeordnetes Werk:	volume:10 ; year:2015 ; number:2 ; day:18 ; month:11 ; pages:125-137

Links:	Volltext

DOI / URN:	10.1007/s12193-015-0203-6

Katalog-ID:	SPR025174053

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520			\|a Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance.
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allfields	10.1007/s12193-015-0203-6 doi (DE-627)SPR025174053 (SPR)s12193-015-0203-6-e DE-627 ger DE-627 rakwb eng Sun, Bo verfasserin aut Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © OpenInterface Association 2015 Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance. Emotion recognition (dpeaa)DE-He213 Multimodal features (dpeaa)DE-He213 Feature-level fusion (dpeaa)DE-He213 Decision-level fusion (dpeaa)DE-He213 Multiple kernel learning (dpeaa)DE-He213 Hierarchical classifier (dpeaa)DE-He213 Li, Liandong aut Wu, Xuewen aut Zuo, Tian aut Chen, Ying aut Zhou, Guoyan aut He, Jun aut Zhu, Xiaoming aut Enthalten in Journal on multimodal user interfaces Berlin : Springer, 2007 10(2015), 2 vom: 18. Nov., Seite 125-137 (DE-627)565519808 (DE-600)2424563-X 1783-8738 nnns volume:10 year:2015 number:2 day:18 month:11 pages:125-137 https://dx.doi.org/10.1007/s12193-015-0203-6 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2015 2 18 11 125-137
spelling	10.1007/s12193-015-0203-6 doi (DE-627)SPR025174053 (SPR)s12193-015-0203-6-e DE-627 ger DE-627 rakwb eng Sun, Bo verfasserin aut Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © OpenInterface Association 2015 Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance. Emotion recognition (dpeaa)DE-He213 Multimodal features (dpeaa)DE-He213 Feature-level fusion (dpeaa)DE-He213 Decision-level fusion (dpeaa)DE-He213 Multiple kernel learning (dpeaa)DE-He213 Hierarchical classifier (dpeaa)DE-He213 Li, Liandong aut Wu, Xuewen aut Zuo, Tian aut Chen, Ying aut Zhou, Guoyan aut He, Jun aut Zhu, Xiaoming aut Enthalten in Journal on multimodal user interfaces Berlin : Springer, 2007 10(2015), 2 vom: 18. Nov., Seite 125-137 (DE-627)565519808 (DE-600)2424563-X 1783-8738 nnns volume:10 year:2015 number:2 day:18 month:11 pages:125-137 https://dx.doi.org/10.1007/s12193-015-0203-6 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2015 2 18 11 125-137
allfields_unstemmed	10.1007/s12193-015-0203-6 doi (DE-627)SPR025174053 (SPR)s12193-015-0203-6-e DE-627 ger DE-627 rakwb eng Sun, Bo verfasserin aut Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © OpenInterface Association 2015 Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance. Emotion recognition (dpeaa)DE-He213 Multimodal features (dpeaa)DE-He213 Feature-level fusion (dpeaa)DE-He213 Decision-level fusion (dpeaa)DE-He213 Multiple kernel learning (dpeaa)DE-He213 Hierarchical classifier (dpeaa)DE-He213 Li, Liandong aut Wu, Xuewen aut Zuo, Tian aut Chen, Ying aut Zhou, Guoyan aut He, Jun aut Zhu, Xiaoming aut Enthalten in Journal on multimodal user interfaces Berlin : Springer, 2007 10(2015), 2 vom: 18. Nov., Seite 125-137 (DE-627)565519808 (DE-600)2424563-X 1783-8738 nnns volume:10 year:2015 number:2 day:18 month:11 pages:125-137 https://dx.doi.org/10.1007/s12193-015-0203-6 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2015 2 18 11 125-137
allfieldsGer	10.1007/s12193-015-0203-6 doi (DE-627)SPR025174053 (SPR)s12193-015-0203-6-e DE-627 ger DE-627 rakwb eng Sun, Bo verfasserin aut Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © OpenInterface Association 2015 Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance. Emotion recognition (dpeaa)DE-He213 Multimodal features (dpeaa)DE-He213 Feature-level fusion (dpeaa)DE-He213 Decision-level fusion (dpeaa)DE-He213 Multiple kernel learning (dpeaa)DE-He213 Hierarchical classifier (dpeaa)DE-He213 Li, Liandong aut Wu, Xuewen aut Zuo, Tian aut Chen, Ying aut Zhou, Guoyan aut He, Jun aut Zhu, Xiaoming aut Enthalten in Journal on multimodal user interfaces Berlin : Springer, 2007 10(2015), 2 vom: 18. Nov., Seite 125-137 (DE-627)565519808 (DE-600)2424563-X 1783-8738 nnns volume:10 year:2015 number:2 day:18 month:11 pages:125-137 https://dx.doi.org/10.1007/s12193-015-0203-6 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2015 2 18 11 125-137
allfieldsSound	10.1007/s12193-015-0203-6 doi (DE-627)SPR025174053 (SPR)s12193-015-0203-6-e DE-627 ger DE-627 rakwb eng Sun, Bo verfasserin aut Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © OpenInterface Association 2015 Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance. Emotion recognition (dpeaa)DE-He213 Multimodal features (dpeaa)DE-He213 Feature-level fusion (dpeaa)DE-He213 Decision-level fusion (dpeaa)DE-He213 Multiple kernel learning (dpeaa)DE-He213 Hierarchical classifier (dpeaa)DE-He213 Li, Liandong aut Wu, Xuewen aut Zuo, Tian aut Chen, Ying aut Zhou, Guoyan aut He, Jun aut Zhu, Xiaoming aut Enthalten in Journal on multimodal user interfaces Berlin : Springer, 2007 10(2015), 2 vom: 18. Nov., Seite 125-137 (DE-627)565519808 (DE-600)2424563-X 1783-8738 nnns volume:10 year:2015 number:2 day:18 month:11 pages:125-137 https://dx.doi.org/10.1007/s12193-015-0203-6 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_65 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_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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 10 2015 2 18 11 125-137
language	English
source	Enthalten in Journal on multimodal user interfaces 10(2015), 2 vom: 18. Nov., Seite 125-137 volume:10 year:2015 number:2 day:18 month:11 pages:125-137
sourceStr	Enthalten in Journal on multimodal user interfaces 10(2015), 2 vom: 18. Nov., Seite 125-137 volume:10 year:2015 number:2 day:18 month:11 pages:125-137
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Emotion recognition Multimodal features Feature-level fusion Decision-level fusion Multiple kernel learning Hierarchical classifier
isfreeaccess_bool	false
container_title	Journal on multimodal user interfaces
authorswithroles_txt_mv	Sun, Bo @@aut@@ Li, Liandong @@aut@@ Wu, Xuewen @@aut@@ Zuo, Tian @@aut@@ Chen, Ying @@aut@@ Zhou, Guoyan @@aut@@ He, Jun @@aut@@ Zhu, Xiaoming @@aut@@
publishDateDaySort_date	2015-11-18T00:00:00Z
hierarchy_top_id	565519808
id	SPR025174053
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR025174053</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230403063948.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2015 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12193-015-0203-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR025174053</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12193-015-0203-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Bo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© OpenInterface Association 2015</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. 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author	Sun, Bo
spellingShingle	Sun, Bo misc Emotion recognition misc Multimodal features misc Feature-level fusion misc Decision-level fusion misc Multiple kernel learning misc Hierarchical classifier Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild
authorStr	Sun, Bo
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topic_title	Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild Emotion recognition (dpeaa)DE-He213 Multimodal features (dpeaa)DE-He213 Feature-level fusion (dpeaa)DE-He213 Decision-level fusion (dpeaa)DE-He213 Multiple kernel learning (dpeaa)DE-He213 Hierarchical classifier (dpeaa)DE-He213
topic	misc Emotion recognition misc Multimodal features misc Feature-level fusion misc Decision-level fusion misc Multiple kernel learning misc Hierarchical classifier
topic_unstemmed	misc Emotion recognition misc Multimodal features misc Feature-level fusion misc Decision-level fusion misc Multiple kernel learning misc Hierarchical classifier
topic_browse	misc Emotion recognition misc Multimodal features misc Feature-level fusion misc Decision-level fusion misc Multiple kernel learning misc Hierarchical classifier
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title	Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild
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title_full	Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild
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author_browse	Sun, Bo Li, Liandong Wu, Xuewen Zuo, Tian Chen, Ying Zhou, Guoyan He, Jun Zhu, Xiaoming
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author-letter	Sun, Bo
doi_str_mv	10.1007/s12193-015-0203-6
title_sort	combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild
title_auth	Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild
abstract	Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance. © OpenInterface Association 2015
abstractGer	Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance. © OpenInterface Association 2015
abstract_unstemmed	Abstract Emotion recognition in the wild is a very challenging task. In this paper, we investigate a variety of different multimodal features (acoustic and visual) from video clips to evaluate their discriminative abilities in human emotion analysis. For each clip, we extract MSDF BoW, LBP-TOP, PHOG, LPQ-TOP and Audio features. We train different classifiers for every type of feature on the AFEW dataset from the ICMI 2014 EmotiW Challenge, and we propose a novel hierarchical classification framework, which combines the feature-level and decision-level fusion strategy for all of the extracted multimodal features. The final achievement we gain on the AFEW test set is 47.17 %, which is considerably better than the best baseline recognition rate of 33.7 %. Among all of the teams participating in the ICMI 2014 EmotiW challenge, our recognition performance won the first runner-up award. Furthermore, we test our method on FERA and CK datasets, the experimental results also show good performance. © OpenInterface Association 2015
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container_issue	2
title_short	Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild
url	https://dx.doi.org/10.1007/s12193-015-0203-6
remote_bool	true
author2	Li, Liandong Wu, Xuewen Zuo, Tian Chen, Ying Zhou, Guoyan He, Jun Zhu, Xiaoming
author2Str	Li, Liandong Wu, Xuewen Zuo, Tian Chen, Ying Zhou, Guoyan He, Jun Zhu, Xiaoming
ppnlink	565519808
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isOA_txt	false
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doi_str	10.1007/s12193-015-0203-6
up_date	2024-07-03T14:20:06.395Z
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Combining feature-level and decision-level fusion in a hierarchical classifier for emotion recognition in the wild

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