Facial Emotion Recognition Using Context Based Multimodal Approach
Priya Metri, Jayshree Ghorpade-Aher, Ayesha Butalia
Abstract
Priya Metri, Jayshree Ghorpade-Aher, Ayesha Butalia
Abstract
Emotions play a crucial role in person to person\ninteraction. In recent years, there has been a growing interest in\nimproving all aspects of interaction between humans and\ncomputers. The ability to understand human emotions is desirable\nfor the computer in several applications especially by observing\nfacial expressions. This paper explores a ways of humancomputer\ninteraction that enable the computer to be more aware\nof the user�s emotional expressions we present a approach for the\nemotion recognition from a facial expression, hand and body\nposture. Our model uses multimodal emotion recognition system\nin which we use two different models for facial expression\nrecognition and for hand and body posture recognition and then\ncombining the result of both classifiers using a third classifier\nwhich give the resulting emotion . Multimodal system gives more\naccurate result than a signal or bimodal system
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Emotions play a crucial role in person to person\ninteraction. In recent years, there has been a growing interest in\nimproving all aspects of interaction between humans and\ncomputers. The ability to understand human emotions is desirable\nfor the computer in several applications especially by observing\nfacial expressions. This paper explores a ways of humancomputer\ninteraction that enable the computer to be more aware\nof the user�s emotional expressions we present a approach for the\nemotion recognition from a facial expression, hand and body\nposture. Our model uses multimodal emotion recognition system\nin which we use two different models for facial expression\nrecognition and for hand and body posture recognition and then\ncombining the result of both classifiers using a third classifier\nwhich give the resulting emotion . Multimodal system gives more\naccurate result than a signal or bimodal system
Key concepts: Facial expression, Emotion recognition, Gesture, Computer science, Classifier (UML), Gesture recognition, Emotion classification, Human–computer interaction