HUMAN BEHAVIOUR ANALYSIS USING CNN.
Emotion recognition has been the subject of extensive research due to its significant impact on various domains, including healthcare, human-computer interaction, and marketing. Traditional methods of emotion recognition rely on visual cues, such as facial expressions, to decipher emotional states....
| Publicado en: | SHS Web of Conferences Vol. 194; pp. 1 - 13 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
EDP Sciences
6/26/2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178581302&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 178581302 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 24165182 FT5R jtl: SHS Web of Conferences issn: 24165182 maglogo: N pubinfo: dt: 6/26/2024 vid: 194 pid: 76090 pub: EDP Sciences artinfo: ui: 178581302 10.1051/shsconf/202419401001 ppf: 1 ppct: 12 formats: tig: atl: HUMAN BEHAVIOUR ANALYSIS USING CNN. aug: au: Budhewar, Anupama Purbuj, Sanika Rathod, Darshika Tukan, Mrunal Kulshrestha, Palak affil: MIT-ADT University MIT ADT Campus, Rajbaugh Loni Kalbhor - 412201 sug: keyword: Affective Computing Algorithmic Approaches Collaborative Environments Deep Learning Models EEG Data Processing Ethical Considerations Facial Expression Analysis Feature Extraction Human-Computer Interaction Individual Differences Informed Consent Machine Learning Algorithms Mental Health Applications Model Training and Optimization Multimodal Emotion Recognition Neuro-feedback Systems Personalized Emotional Profiling Privacy Protection Real-time Emotion Detection Virtual Reality Integration ab: Emotion recognition has been the subject of extensive research due to its significant impact on various domains, including healthcare, human-computer interaction, and marketing. Traditional methods of emotion recognition rely on visual cues, such as facial expressions, to decipher emotional states. However, these methods often fall short when dealing with individuals who have limited ability to express emotions through facial expressions, such as individuals with certain neurological disorders. This research paper proposes a novel approach to emotion recognition by combining facial expression analysis with electroencephalography (EEG) data. Deep learning techniques are applied to extract features from facial expressions captured through video analysis, while simultaneously analyzing the corresponding EEG signals. The goal is to improve emotion recognition accuracy by utilizing the complementary information offered by the interaction between facial expressions and EEG data. Emotion recognition is a challenging task that has collected considerable recognition in the current years. Different and refined approaches to recognize emotions based on facial expressions, voice analysis, physiological signals, and behavioral patterns have been developed. While facial expression analysis has been a dominant approach, it falls short in instances where individuals cannot effectively express emotions through their faces. To overcome these limitations, there is a need to explore alternative methods that can provide a more accurate assessment of emotions. This research paper aims to investigate the collaboration and interaction between facial expressions and EEG data for emotion recognition. By combining the information from both modalities, it is expected to augment the accuracy and strength of emotion recognition systems. The proposed method can range from conducting literature reviews to designing and fine-tuning deep learning models for feature extraction, developing fusion models to combine features from facial expressions and EEG data, performing experimentation and evaluation, writing papers and documentation, preparing presentations for dissemination, and engaging in regular meetings and discussions for effective collaboration. Ethical considerations, robustness and generalizability, continual learning and skill development, and utilizing collaboration tools and platforms are also essential contributions to ensure the project's success. pubtype: Conference Proceedings doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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