Performance analysis of emotion detection using machine learning algorithms
This project’s goal is not just to identify emotions, but also to develop methods for training and evaluating how well various machine learning models recognize emotions. This study investigates how different facial emotions (happy, fear, neutral, sad, angry, astonished) may be recognized using feat...
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| Published in | AIP conference proceedings Vol. 3075; no. 1 |
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| Main Authors | , , |
| Format | Journal Article Conference Proceeding |
| Language | English |
| Published |
Melville
American Institute of Physics
29.07.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0094-243X 1551-7616 |
| DOI | 10.1063/5.0217097 |
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| Abstract | This project’s goal is not just to identify emotions, but also to develop methods for training and evaluating how well various machine learning models recognize emotions. This study investigates how different facial emotions (happy, fear, neutral, sad, angry, astonished) may be recognized using feature extraction from face expressions in conjunction with a neural network combination. Convolutional neural networks, decision trees, logistic regression, and K-Nearest Neighbors will be among the several models investigated. The trial results shown that the recommended model is more accurate than 80% at identifying emotions. |
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| AbstractList | This project’s goal is not just to identify emotions, but also to develop methods for training and evaluating how well various machine learning models recognize emotions. This study investigates how different facial emotions (happy, fear, neutral, sad, angry, astonished) may be recognized using feature extraction from face expressions in conjunction with a neural network combination. Convolutional neural networks, decision trees, logistic regression, and K-Nearest Neighbors will be among the several models investigated. The trial results shown that the recommended model is more accurate than 80% at identifying emotions. |
| Author | Raj, Kundan Jatin Vidhya, R. |
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| DOI | 10.1063/5.0217097 |
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| Editor | Kishore Anthuvan Sahayaraj, K Godfrey Winster, S Baskar, M Pushpalatha, M |
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| References_xml | – volume: 18 start-page: 32 year: 2001 ident: c28 article-title: Emotion recognition in human– computer interaction publication-title: IEEE Signal Process Mag – start-page: 1 year: 2019 2019 ident: c14 article-title: Deeply learned pose invariant image analysis with applications in 3D face recognition publication-title: Math Probl Eng – volume: 14 start-page: 7 year: 2019 ident: c8 article-title: Modeling globalgeometricspatial information forrotation invariant classification of satellite images publication-title: PLoS ONE – volume: 115 start-page: 102 year: 1994 ident: c2 article-title: Is there universal recognition of emotion from facial expression? A review of the cross-cultural studies publication-title: PsycholBull – volume: 13 start-page: e0203339 year: 2018 ident: c7 article-title: A hybrid geometric spatial image representation forscene classification publication-title: PLoS ONE – volume: 11 start-page: e0157428 year: 2016 ident: c9 article-title: A novel image retrieval based on visual words integration of SIFT and SURF publication-title: PLoS ONE – volume: 21 start-page: 211 year: 2018 ident: c17 article-title: Facial expression recognition using hierarchical features with deep comprehensive multipatches aggre-gation convolutional neural networks publication-title: IEEE Trans Multimedia – start-page: 1 year: 2019 2019 ident: c12 article-title: The impact of asymmetric left and asymmetric right face images on accurate age estimation publication-title: Math Probl Eng – volume: 17 start-page: 712 year: 2017 ident: c4 article-title: Facial expression recognitionwith fusion features extracted from salient facial areas publication-title: Sensors – volume: 12 start-page: 10 year: 2018 ident: c16 article-title: Pose and expression invariant alignment based multi-view 3D face recognition publication-title: KSII Trans Internet Inf Syst – volume: 24 start-page: 696 year: 2002 ident: c29 article-title: Face detection in color images publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 16 start-page: 363 year: 1992 ident: c11 article-title: More evidence for the universality of a contempt expression publication-title: Motiv Emot – volume: 79 start-page: 109 year: 1934 ident: c25 article-title: Evolution of facial musculature and facial expression publication-title: J Nerv Ment Dis – volume: 51 start-page: 2 year: 2012 ident: c26 article-title: Morphology based facial feature extraction and facial expression recognition for driver vigilance publication-title: Int J Comput Appl – volume: 8 start-page: 2242 year: 2018 ident: c6 article-title: A novel discriminating and relative global spatial image representation with applications in CBIR publication-title: Appl Sci – start-page: 1 year: 2018 2018 ident: c15 article-title: Data augmentation-assisted makeup-invariant face recognition publication-title: Math Probl Eng – volume: 97 start-page: 103 year: 2005 ident: c22 article-title: Recent advances in visual and infrared face recognition— a review publication-title: Comput Vis Image Underst – volume: 16 start-page: 1729881419851716 year: 2019 ident: c13 article-title: Three-dimensional face recognition using variance-based registration and subject-specific descriptors publication-title: Int J Adv Robot Syst – volume: 17 start-page: 124 year: 1971 ident: c10 article-title: Constants across cultures in the faceand emotion publication-title: J Personal Soc Psychol – volume: 1 start-page: 221 year: 2006 ident: c20 article-title: Understanding chimpanzee facial expression: insights into the evolution of communication publication-title: SocCogn Affect Neurosci |
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| SubjectTerms | Algorithms Artificial neural networks Decision trees Emotion recognition Emotions Feature recognition Machine learning Regression models |
| Title | Performance analysis of emotion detection using machine learning algorithms |
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