FL-CapsNet: facial localization augmented capsule network for human emotion recognition

Abstract Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using...
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Autor*in:	Sivaiah, Bellamkonda [verfasserIn] Gopalan, N. P. Mala, C. Lavanya, Settipalli

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Facial emotions Emotion recognition Expression recognition Capsule networks Facial localization

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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: Signal, image and video processing - London [u.a.] : Springer, 2007, 17(2022), 4 vom: 17. Okt., Seite 1705-1713
Übergeordnetes Werk:	volume:17 ; year:2022 ; number:4 ; day:17 ; month:10 ; pages:1705-1713

Links:	Volltext

DOI / URN:	10.1007/s11760-022-02381-2

Katalog-ID:	SPR050164627

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520			\|a Abstract Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models.
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allfields	10.1007/s11760-022-02381-2 doi (DE-627)SPR050164627 (SPR)s11760-022-02381-2-e DE-627 ger DE-627 rakwb eng Sivaiah, Bellamkonda verfasserin aut FL-CapsNet: facial localization augmented capsule network for human emotion recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models. Facial emotions (dpeaa)DE-He213 Emotion recognition (dpeaa)DE-He213 Expression recognition (dpeaa)DE-He213 Capsule networks (dpeaa)DE-He213 Facial localization (dpeaa)DE-He213 Gopalan, N. P. aut Mala, C. aut Lavanya, Settipalli aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2022), 4 vom: 17. Okt., Seite 1705-1713 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2022 number:4 day:17 month:10 pages:1705-1713 https://dx.doi.org/10.1007/s11760-022-02381-2 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_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_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 17 2022 4 17 10 1705-1713
spelling	10.1007/s11760-022-02381-2 doi (DE-627)SPR050164627 (SPR)s11760-022-02381-2-e DE-627 ger DE-627 rakwb eng Sivaiah, Bellamkonda verfasserin aut FL-CapsNet: facial localization augmented capsule network for human emotion recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models. Facial emotions (dpeaa)DE-He213 Emotion recognition (dpeaa)DE-He213 Expression recognition (dpeaa)DE-He213 Capsule networks (dpeaa)DE-He213 Facial localization (dpeaa)DE-He213 Gopalan, N. P. aut Mala, C. aut Lavanya, Settipalli aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2022), 4 vom: 17. Okt., Seite 1705-1713 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2022 number:4 day:17 month:10 pages:1705-1713 https://dx.doi.org/10.1007/s11760-022-02381-2 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_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_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 17 2022 4 17 10 1705-1713
allfields_unstemmed	10.1007/s11760-022-02381-2 doi (DE-627)SPR050164627 (SPR)s11760-022-02381-2-e DE-627 ger DE-627 rakwb eng Sivaiah, Bellamkonda verfasserin aut FL-CapsNet: facial localization augmented capsule network for human emotion recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models. Facial emotions (dpeaa)DE-He213 Emotion recognition (dpeaa)DE-He213 Expression recognition (dpeaa)DE-He213 Capsule networks (dpeaa)DE-He213 Facial localization (dpeaa)DE-He213 Gopalan, N. P. aut Mala, C. aut Lavanya, Settipalli aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2022), 4 vom: 17. Okt., Seite 1705-1713 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2022 number:4 day:17 month:10 pages:1705-1713 https://dx.doi.org/10.1007/s11760-022-02381-2 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_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_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 17 2022 4 17 10 1705-1713
allfieldsGer	10.1007/s11760-022-02381-2 doi (DE-627)SPR050164627 (SPR)s11760-022-02381-2-e DE-627 ger DE-627 rakwb eng Sivaiah, Bellamkonda verfasserin aut FL-CapsNet: facial localization augmented capsule network for human emotion recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models. Facial emotions (dpeaa)DE-He213 Emotion recognition (dpeaa)DE-He213 Expression recognition (dpeaa)DE-He213 Capsule networks (dpeaa)DE-He213 Facial localization (dpeaa)DE-He213 Gopalan, N. P. aut Mala, C. aut Lavanya, Settipalli aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2022), 4 vom: 17. Okt., Seite 1705-1713 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2022 number:4 day:17 month:10 pages:1705-1713 https://dx.doi.org/10.1007/s11760-022-02381-2 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_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_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 17 2022 4 17 10 1705-1713
allfieldsSound	10.1007/s11760-022-02381-2 doi (DE-627)SPR050164627 (SPR)s11760-022-02381-2-e DE-627 ger DE-627 rakwb eng Sivaiah, Bellamkonda verfasserin aut FL-CapsNet: facial localization augmented capsule network for human emotion recognition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models. Facial emotions (dpeaa)DE-He213 Emotion recognition (dpeaa)DE-He213 Expression recognition (dpeaa)DE-He213 Capsule networks (dpeaa)DE-He213 Facial localization (dpeaa)DE-He213 Gopalan, N. P. aut Mala, C. aut Lavanya, Settipalli aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 17(2022), 4 vom: 17. Okt., Seite 1705-1713 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:17 year:2022 number:4 day:17 month:10 pages:1705-1713 https://dx.doi.org/10.1007/s11760-022-02381-2 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_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_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 17 2022 4 17 10 1705-1713
language	English
source	Enthalten in Signal, image and video processing 17(2022), 4 vom: 17. Okt., Seite 1705-1713 volume:17 year:2022 number:4 day:17 month:10 pages:1705-1713
sourceStr	Enthalten in Signal, image and video processing 17(2022), 4 vom: 17. Okt., Seite 1705-1713 volume:17 year:2022 number:4 day:17 month:10 pages:1705-1713
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Facial emotions Emotion recognition Expression recognition Capsule networks Facial localization
isfreeaccess_bool	false
container_title	Signal, image and video processing
authorswithroles_txt_mv	Sivaiah, Bellamkonda @@aut@@ Gopalan, N. P. @@aut@@ Mala, C. @@aut@@ Lavanya, Settipalli @@aut@@
publishDateDaySort_date	2022-10-17T00:00:00Z
hierarchy_top_id	546899102
id	SPR050164627
language_de	englisch
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author	Sivaiah, Bellamkonda
spellingShingle	Sivaiah, Bellamkonda misc Facial emotions misc Emotion recognition misc Expression recognition misc Capsule networks misc Facial localization FL-CapsNet: facial localization augmented capsule network for human emotion recognition
authorStr	Sivaiah, Bellamkonda
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topic_title	FL-CapsNet: facial localization augmented capsule network for human emotion recognition Facial emotions (dpeaa)DE-He213 Emotion recognition (dpeaa)DE-He213 Expression recognition (dpeaa)DE-He213 Capsule networks (dpeaa)DE-He213 Facial localization (dpeaa)DE-He213
topic	misc Facial emotions misc Emotion recognition misc Expression recognition misc Capsule networks misc Facial localization
topic_unstemmed	misc Facial emotions misc Emotion recognition misc Expression recognition misc Capsule networks misc Facial localization
topic_browse	misc Facial emotions misc Emotion recognition misc Expression recognition misc Capsule networks misc Facial localization
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title	FL-CapsNet: facial localization augmented capsule network for human emotion recognition
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title_full	FL-CapsNet: facial localization augmented capsule network for human emotion recognition
author_sort	Sivaiah, Bellamkonda
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author_browse	Sivaiah, Bellamkonda Gopalan, N. P. Mala, C. Lavanya, Settipalli
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author-letter	Sivaiah, Bellamkonda
doi_str_mv	10.1007/s11760-022-02381-2
title_sort	fl-capsnet: facial localization augmented capsule network for human emotion recognition
title_auth	FL-CapsNet: facial localization augmented capsule network for human emotion recognition
abstract	Abstract Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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 Emotion is an important aspect of effective human communication, and hence, facial emotion recognition (FER) has become essential in human–computer interaction systems. The automation of FER has been carried out by many researchers using ML/DL techniques. However, the models developed using convolutional neural networks (CNNs) bagged high recognition accuracies among different FER approaches. Rather than its high performance, CNN has failed to encode different orientation features since the pooling operations used in CNN for feature extraction omit vital information. Due to omitting vital features, the performance will be reduced while recognizing the emotions from facial images that consists of different orientations. Subsequently, to reduce the problems of CNN such as encoding different orientation features and increased training time, Capsule Networks (CapsNet) were developed. CapsNet is capable of storing 8 such features vectors with the incorporation of dynamic routing approaches and squashing in place of pooling operations to mitigate the issue of rotational invariance. Hence in this paper, we proposed CapsNet for FER in order to enhance the accuracy. However, the facial images that consider for training consist of unwanted information that is not essential for FER, delay the convergence and take more iterations for training facial images. Hence, face localization (FL) is proposed to incorporate with CapsNet in our model to eliminate the back ground noise or unwanted information from the facial images for effective training process. The proposed FL-CapsNet is rigorously tested on benchmark datasets such as JAFFE, CK+, and FER2013 to evaluate the generalization of the proposed model, and it is evidenced that FL-CapsNet outperformed the existing CapsNet-based FER models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. 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	FL-CapsNet: facial localization augmented capsule network for human emotion recognition
url	https://dx.doi.org/10.1007/s11760-022-02381-2
remote_bool	true
author2	Gopalan, N. P. Mala, C. Lavanya, Settipalli
author2Str	Gopalan, N. P. Mala, C. Lavanya, Settipalli
ppnlink	546899102
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isOA_txt	false
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doi_str	10.1007/s11760-022-02381-2
up_date	2024-07-03T13:47:30.182Z
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