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Enhanced global and local face feature extraction for effective recognition of facial emotions.

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Title: Enhanced global and local face feature extraction for effective recognition of facial emotions.
Authors: Retnamony, Jeen Retna Kumar, Muniasamy, Sundaram, Stanley, Berakhah Florence
Source: Concurrency & Computation: Practice & Experience; 2/28/2022, Vol. 34 Issue 5, p1-25, 25p
Subject Terms: EMOTION recognition, BOLTZMANN machine, EMOTIONS, FEATURE extraction, STATISTICAL correlation, SOCIAL interaction
Abstract: Emotion recognition is a challenging task in the field of human computer interaction. For a successful human emotion recognition system, a robust, discriminative, and sensitive feature extraction is an essential need. In this article, the extraction of global features is done by the proposed frequency decoded lifting wavelet pattern descriptor (FDLWP) and extraction of local features is done by the proposed local gradient difference zig‐zag pattern descriptor (LGDZP). The face parts are detected using viola jones algorithm and the selection of optimal face active regions is accomplished by the calculation of structural similarity index measure. Eventually the local spatial zig‐zag structure of the face region is utilized to attain LGDZP descriptor. The fusion of local and global features is accomplished using canonical correlation analysis. The classification made using bank of restricted Boltzmann machine (RBM) classifiers yields promising results for the proposed method. Furthermore, the proposed recognition method delivers a promising accuracy in varying illumination, occlusion, and noise. The accuracy of the method is analyzed by doing experiments with the databases such as JAFFE, CK+, MMI, Oulu‐CASIA, and SFEW. The obtained results of the proposed method yield better accuracy than the existing state of art methods in this field. [ABSTRACT FROM AUTHOR]
Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhanced global and local face feature extraction for effective recognition of facial emotions.
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  Data: <searchLink fieldCode="AR" term="%22Retnamony%2C+Jeen+Retna+Kumar%22">Retnamony, Jeen Retna Kumar</searchLink><br /><searchLink fieldCode="AR" term="%22Muniasamy%2C+Sundaram%22">Muniasamy, Sundaram</searchLink><br /><searchLink fieldCode="AR" term="%22Stanley%2C+Berakhah+Florence%22">Stanley, Berakhah Florence</searchLink>
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  Data: Concurrency & Computation: Practice & Experience; 2/28/2022, Vol. 34 Issue 5, p1-25, 25p
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  Data: <searchLink fieldCode="DE" term="%22EMOTION+recognition%22">EMOTION recognition</searchLink><br /><searchLink fieldCode="DE" term="%22BOLTZMANN+machine%22">BOLTZMANN machine</searchLink><br /><searchLink fieldCode="DE" term="%22EMOTIONS%22">EMOTIONS</searchLink><br /><searchLink fieldCode="DE" term="%22FEATURE+extraction%22">FEATURE extraction</searchLink><br /><searchLink fieldCode="DE" term="%22STATISTICAL+correlation%22">STATISTICAL correlation</searchLink><br /><searchLink fieldCode="DE" term="%22SOCIAL+interaction%22">SOCIAL interaction</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Emotion recognition is a challenging task in the field of human computer interaction. For a successful human emotion recognition system, a robust, discriminative, and sensitive feature extraction is an essential need. In this article, the extraction of global features is done by the proposed frequency decoded lifting wavelet pattern descriptor (FDLWP) and extraction of local features is done by the proposed local gradient difference zig‐zag pattern descriptor (LGDZP). The face parts are detected using viola jones algorithm and the selection of optimal face active regions is accomplished by the calculation of structural similarity index measure. Eventually the local spatial zig‐zag structure of the face region is utilized to attain LGDZP descriptor. The fusion of local and global features is accomplished using canonical correlation analysis. The classification made using bank of restricted Boltzmann machine (RBM) classifiers yields promising results for the proposed method. Furthermore, the proposed recognition method delivers a promising accuracy in varying illumination, occlusion, and noise. The accuracy of the method is analyzed by doing experiments with the databases such as JAFFE, CK+, MMI, Oulu‐CASIA, and SFEW. The obtained results of the proposed method yield better accuracy than the existing state of art methods in this field. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Concurrency & Computation: Practice & Experience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1002/cpe.6701
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      – Code: eng
        Text: English
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        PageCount: 25
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      – SubjectFull: EMOTION recognition
        Type: general
      – SubjectFull: BOLTZMANN machine
        Type: general
      – SubjectFull: EMOTIONS
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      – SubjectFull: FEATURE extraction
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      – SubjectFull: STATISTICAL correlation
        Type: general
      – SubjectFull: SOCIAL interaction
        Type: general
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      – TitleFull: Enhanced global and local face feature extraction for effective recognition of facial emotions.
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            NameFull: Retnamony, Jeen Retna Kumar
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            NameFull: Muniasamy, Sundaram
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            NameFull: Stanley, Berakhah Florence
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            – D: 28
              M: 02
              Text: 2/28/2022
              Type: published
              Y: 2022
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              Value: 34
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            – TitleFull: Concurrency & Computation: Practice & Experience
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