EMOTION RECOGNITION USING PUPILLOMETRY AND DEEP LEARNING: A SUMMARY.
This research explores how pupillometry can be used to recognize emotions by studying changes in pupil size in reaction to emotional cues. Sophisticated machine learning models such as CNNs, transformer-based structures, and ensemble methods are utilized to tackle the difficulties of real-time appli...
| Publicado en: | InSight: Rivier Academic Journal Vol. 20; no. 1; pp. 1 - 10 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Rivier College (InSight: Rivier Academic Journal)
Fall2025
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| Materias: | |
| 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=192247936&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 192247936 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 19469233 B2BS jtl: InSight: Rivier Academic Journal issn: 19469233 maglogo: N pubinfo: dt: Fall2025 vid: 20 iid: 1 pid: 59866 pub: Rivier College (InSight: Rivier Academic Journal) artinfo: ui: 192247936 ppf: 1 ppct: 9 formats: tig: atl: EMOTION RECOGNITION USING PUPILLOMETRY AND DEEP LEARNING: A SUMMARY. aug: au: Gorantla, Venkatasai Vasantada, Anoohya Arthimalla, Bhavanika Barker, Darlien affil: Graduate students, Computer Science Department, Rivier University Assistant Professor, Computer Science Department, Rivier University su: Pupillometry Deep learning Multimodal user interfaces Machine learning Emotion recognition Adaptive control systems Real-time computing Neurophysiology sug: subj: Pupillometry Deep learning Multimodal user interfaces Machine learning Emotion recognition Adaptive control systems Real-time computing Neurophysiology keyword: Convolutional Neural Networks (CNN) Deep Learning Emotion Recognition Feature Extraction Human-Computer Interaction Machine Learning Pupil Dilation Real-Time Processing ab: This research explores how pupillometry can be used to recognize emotions by studying changes in pupil size in reaction to emotional cues. Sophisticated machine learning models such as CNNs, transformer-based structures, and ensemble methods are utilized to tackle the difficulties of real-time applications. The study emphasizes the benefits of incorporating pupillometry into multimodal frameworks that incorporate other physiological signals like heart rate variability and skin conductance to enhance the reliability and precision of emotion recognition systems. The study's main contributions are the advancement of adaptive systems that can react to real-time emotions, the establishment of datasets for training and validation, and the investigation of multimodal techniques to improve performance in different scenarios. Moreover, the results highlight the possibility of using pupillometry-based models in areas like mental health, education, and human-computer interaction, where personalized and adaptive emotion recognition can make a big difference. This study sets the foundation for future progress in adaptive emotion recognition systems by tackling challenges in scalability, environmental variability, and data diversity. Combining pupillometry with advanced machine learning methods shows potential for improving emotional intelligence in technology, opening opportunities for use in virtual reality, therapy, and adaptive interfaces in real-world scenarios. This research helps advance the field of affective computing by offering a detailed structure for identifying emotions using nonintrusive physiological measures. Our contribution to this paper is to summarize current research on pupillometry for emotion recognition, emphasizing deep learning approaches, multimodal integration, and real-time application challenges. We highlight advancements, limitations, and future research directions to enhance emotion-aware systems in healthcare, education, and human-computer interaction. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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