Academic Journal
View in EDS

Mental Health State Classification Using Facial Emotion Recognition and Detection

Bibliographic Details
Title: Mental Health State Classification Using Facial Emotion Recognition and Detection
Authors: Al-zanam, Adel Aref Ali, Hussein Alhomery, Omer Hussein Abdou Elsayed, Tan, Choo Peng
Source: International Journal on Advanced Science, Engineering and Information Technology. 13:2274-2281
Publisher Information: Insight Society, 2023.
Publication Year: 2023
Subject Terms: Artificial neural network, Artificial intelligence, Facial expression, Support vector machine, Feature (linguistics), Social Sciences, Experimental and Cognitive Psychology, Convolutional neural network, Pattern recognition (psychology), 7. Clean energy, QA273-280 Probabilities. Mathematical statistics, Field (mathematics), Machine learning, FOS: Mathematics, Psychology, 10. No inequality, Preprocessor, Perceptron, Pure mathematics, Linguistics, Computer science, FOS: Philosophy, ethics and religion, 3. Good health, FOS: Psychology, Facial Expression, Philosophy, Emotion Recognition, Affective Computing, Emotion classification, FOS: Languages and literature, Feature extraction, Mathematics, Emotion Recognition and Analysis in Multimodal Data
Description: Analyzing and understanding emotion can help in various aspects, such as realizing one’s attitude, behavior, etc. By understanding one’s emotions, one's mental health state can be calculated, which can help in the medical field by classifying whether one is mentally stable or not. Facial Recognition is one of the many fields of computer vision that utilizes convolutional networks or Conv Nets to perform, train, and learn. Conv Nets and other machine learning algorithms have evolved to adapt better to larger datasets. One of the advancements in Conv Nets and machines is the introduction of various Conv architectures like VGGNet. Thus, this study will present a mental health state classification approach based on facial emotion recognition. The methodology comprises several interconnected components, including preprocessing, feature extraction using Principal Component Analysis (PCA) and VGGNet, and classification using Support Vector Machines (SVM) and Multilayer Perceptron (MLP). The FER2013 dataset tests multiple models’ performances, and the best model is employed in the mental health state classification. The best model, which combines Visual Geometry Group Network (VGGNet) feature extraction with SVM classification, achieved an accuracy of 66%, demonstrating the effectiveness of the proposed methodology. By leveraging facial emotion recognition and machine learning techniques, the study aims to develop an effective method.
Document Type: Article
Other literature type
File Description: text
ISSN: 2460-6952
2088-5334
DOI: 10.18517/ijaseit.v13i6.19055
DOI: 10.18517/ijaseit.13.6.19055
DOI: 10.60692/nz9c9-rzc70
DOI: 10.60692/0c6rp-tkk11
DOI: 10.60692/d0hkf-xa677
DOI: 10.60692/4769n-60349
Rights: CC BY
Accession Number: edsair.doi.dedup.....5b6b51a727ea4ad1da52ddc9da078fa1
Database: OpenAIRE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edsair&genre=article&issn=24606952&ISBN=&volume=13&issue=&date=20231231&spage=2274&pages=2274-2281&title=International Journal on Advanced Science, Engineering and Information Technology&atitle=Mental%20Health%20State%20Classification%20Using%20Facial%20Emotion%20Recognition%20and%20Detection&aulast=Al-zanam%2C%20Adel%20Aref%20Ali&id=DOI:10.18517/ijaseit.v13i6.19055
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: edsair
DbLabel: OpenAIRE
An: edsair.doi.dedup.....5b6b51a727ea4ad1da52ddc9da078fa1
RelevancyScore: 956
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 955.558349609375
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Mental Health State Classification Using Facial Emotion Recognition and Detection
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Al-zanam%2C+Adel+Aref+Ali%22">Al-zanam, Adel Aref Ali</searchLink><br /><searchLink fieldCode="AR" term="%22Hussein+Alhomery%2C+Omer+Hussein+Abdou+Elsayed%22">Hussein Alhomery, Omer Hussein Abdou Elsayed</searchLink><br /><searchLink fieldCode="AR" term="%22Tan%2C+Choo+Peng%22">Tan, Choo Peng</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>International Journal on Advanced Science, Engineering and Information Technology</i>. 13:2274-2281
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: Insight Society, 2023.
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2023
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+network%22">Artificial neural network</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Facial+expression%22">Facial expression</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machine%22">Support vector machine</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+%28linguistics%29%22">Feature (linguistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Social+Sciences%22">Social Sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Experimental+and+Cognitive+Psychology%22">Experimental and Cognitive Psychology</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+network%22">Convolutional neural network</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+%28psychology%29%22">Pattern recognition (psychology)</searchLink><br /><searchLink fieldCode="DE" term="%227%2E+Clean+energy%22">7. Clean energy</searchLink><br /><searchLink fieldCode="DE" term="%22QA273-280+Probabilities%2E+Mathematical+statistics%22">QA273-280 Probabilities. Mathematical statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Field+%28mathematics%29%22">Field (mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22FOS%3A+Mathematics%22">FOS: Mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Psychology%22">Psychology</searchLink><br /><searchLink fieldCode="DE" term="%2210%2E+No+inequality%22">10. No inequality</searchLink><br /><searchLink fieldCode="DE" term="%22Preprocessor%22">Preprocessor</searchLink><br /><searchLink fieldCode="DE" term="%22Perceptron%22">Perceptron</searchLink><br /><searchLink fieldCode="DE" term="%22Pure+mathematics%22">Pure mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistics%22">Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+science%22">Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22FOS%3A+Philosophy%2C+ethics+and+religion%22">FOS: Philosophy, ethics and religion</searchLink><br /><searchLink fieldCode="DE" term="%223%2E+Good+health%22">3. Good health</searchLink><br /><searchLink fieldCode="DE" term="%22FOS%3A+Psychology%22">FOS: Psychology</searchLink><br /><searchLink fieldCode="DE" term="%22Facial+Expression%22">Facial Expression</searchLink><br /><searchLink fieldCode="DE" term="%22Philosophy%22">Philosophy</searchLink><br /><searchLink fieldCode="DE" term="%22Emotion+Recognition%22">Emotion Recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Affective+Computing%22">Affective Computing</searchLink><br /><searchLink fieldCode="DE" term="%22Emotion+classification%22">Emotion classification</searchLink><br /><searchLink fieldCode="DE" term="%22FOS%3A+Languages+and+literature%22">FOS: Languages and literature</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics%22">Mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Emotion+Recognition+and+Analysis+in+Multimodal+Data%22">Emotion Recognition and Analysis in Multimodal Data</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Analyzing and understanding emotion can help in various aspects, such as realizing one’s attitude, behavior, etc. By understanding one’s emotions, one's mental health state can be calculated, which can help in the medical field by classifying whether one is mentally stable or not. Facial Recognition is one of the many fields of computer vision that utilizes convolutional networks or Conv Nets to perform, train, and learn. Conv Nets and other machine learning algorithms have evolved to adapt better to larger datasets. One of the advancements in Conv Nets and machines is the introduction of various Conv architectures like VGGNet. Thus, this study will present a mental health state classification approach based on facial emotion recognition. The methodology comprises several interconnected components, including preprocessing, feature extraction using Principal Component Analysis (PCA) and VGGNet, and classification using Support Vector Machines (SVM) and Multilayer Perceptron (MLP). The FER2013 dataset tests multiple models’ performances, and the best model is employed in the mental health state classification. The best model, which combines Visual Geometry Group Network (VGGNet) feature extraction with SVM classification, achieved an accuracy of 66%, demonstrating the effectiveness of the proposed methodology. By leveraging facial emotion recognition and machine learning techniques, the study aims to develop an effective method.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Article<br />Other literature type
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: text
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2460-6952<br />2088-5334
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.18517/ijaseit.v13i6.19055
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.18517/ijaseit.13.6.19055
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.60692/nz9c9-rzc70
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.60692/0c6rp-tkk11
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.60692/d0hkf-xa677
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.60692/4769n-60349
– Name: Copyright
  Label: Rights
  Group: Cpyrght
  Data: CC BY
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsair.doi.dedup.....5b6b51a727ea4ad1da52ddc9da078fa1
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsair&AN=edsair.doi.dedup.....5b6b51a727ea4ad1da52ddc9da078fa1
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.18517/ijaseit.v13i6.19055
    Languages:
      – Text: Undetermined
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 2274
    Subjects:
      – SubjectFull: Artificial neural network
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Facial expression
        Type: general
      – SubjectFull: Support vector machine
        Type: general
      – SubjectFull: Feature (linguistics)
        Type: general
      – SubjectFull: Social Sciences
        Type: general
      – SubjectFull: Experimental and Cognitive Psychology
        Type: general
      – SubjectFull: Convolutional neural network
        Type: general
      – SubjectFull: Pattern recognition (psychology)
        Type: general
      – SubjectFull: 7. Clean energy
        Type: general
      – SubjectFull: QA273-280 Probabilities. Mathematical statistics
        Type: general
      – SubjectFull: Field (mathematics)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: FOS: Mathematics
        Type: general
      – SubjectFull: Psychology
        Type: general
      – SubjectFull: 10. No inequality
        Type: general
      – SubjectFull: Preprocessor
        Type: general
      – SubjectFull: Perceptron
        Type: general
      – SubjectFull: Pure mathematics
        Type: general
      – SubjectFull: Linguistics
        Type: general
      – SubjectFull: Computer science
        Type: general
      – SubjectFull: FOS: Philosophy, ethics and religion
        Type: general
      – SubjectFull: 3. Good health
        Type: general
      – SubjectFull: FOS: Psychology
        Type: general
      – SubjectFull: Facial Expression
        Type: general
      – SubjectFull: Philosophy
        Type: general
      – SubjectFull: Emotion Recognition
        Type: general
      – SubjectFull: Affective Computing
        Type: general
      – SubjectFull: Emotion classification
        Type: general
      – SubjectFull: FOS: Languages and literature
        Type: general
      – SubjectFull: Feature extraction
        Type: general
      – SubjectFull: Mathematics
        Type: general
      – SubjectFull: Emotion Recognition and Analysis in Multimodal Data
        Type: general
    Titles:
      – TitleFull: Mental Health State Classification Using Facial Emotion Recognition and Detection
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Al-zanam, Adel Aref Ali
      – PersonEntity:
          Name:
            NameFull: Hussein Alhomery, Omer Hussein Abdou Elsayed
      – PersonEntity:
          Name:
            NameFull: Tan, Choo Peng
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 31
              M: 12
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 24606952
            – Type: issn-print
              Value: 20885334
            – Type: issn-locals
              Value: edsair
            – Type: issn-locals
              Value: edsairFT
          Numbering:
            – Type: volume
              Value: 13
          Titles:
            – TitleFull: International Journal on Advanced Science, Engineering and Information Technology
              Type: main
ResultId 1