Human Emotion Detection Through First Person View
Human emotion detection is a rapidly evolving field with critical applications in affective computing, human-computer interaction, and social robotics. While traditional approaches rely heavily on facial expressions and speech analysis, recent advancements have shown that body posture and motion dyn...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/431674 |
| Acceso en línea: | https://hdl.handle.net/2117/431674 |
| Access Level: | acceso abierto |
| Palabra clave: | Computer vision Deep learning (Machine learning) Emotions Visió per computador aprenentatge profund models per visió transformers BoLD Ego4D deep learning vision models time series analysis Visió per ordinador Aprenentatge profund (Aprenentatge automàtic) Emocions Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
| Sumario: | Human emotion detection is a rapidly evolving field with critical applications in affective computing, human-computer interaction, and social robotics. While traditional approaches rely heavily on facial expressions and speech analysis, recent advancements have shown that body posture and motion dynamics can provide significant insights into emotional states. In this study, we explore emotion recognition from a first-person view (FPV) perspective using body pose estimation. FPV introduces unique challenges due to its dynamic and limited field of vision compared to third-person (exo-view) data. Our approach involves training a vision transformer model on the BoLD (Body Language Dataset) and evaluating its performance on the Ego4D dataset, particularly within the social interactions subset. The research investigates how pose estimation models can be adapted to FPV data, the key differences between FPV and exo-view body posture dynamics, and the role of context (environment, social roles) in emotion recognition. By leveraging skeletal trajectories extracted from FPV videos, we aim to identify crucial markers for emotional differentiation and assess the feasibility of posture-based emotion recognition as an alternative to facial and speech-based models. Our findings contribute to the growing field of affective computing, offering novel insights into the intersection of computer vision, human behavior analysis, and deep learning |
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