Visible to the public Emotion Recognition Using Feature-level Fusion of Facial Expressions and Body Gestures

TitleEmotion Recognition Using Feature-level Fusion of Facial Expressions and Body Gestures
Publication TypeConference Paper
Year of Publication2019
AuthorsKeshari, Tanya, Palaniswamy, Suja
Conference Name2019 International Conference on Communication and Electronics Systems (ICCES)
Date PublishedJuly 2019
ISBN Number978-1-7281-1261-9
Keywordsautomatic emotion recognition, bimodal emotion recognition, body gestures, collective visual conduits, Computer vision, Conferences, EEG signals, emotion recognition, Face, face recognition, facial emotion recognition, facial expressions, facial recognition, feature extraction, feature-level, Feature-level fusion, gesture recognition, hog feature, Human Behavior, human emotional behaviour, human factors, human robot interaction, image fusion, Metrics, psychological research findings, psychology, pubcrawl, real-world applications, resilience, Resiliency, sign language recognition, Support vector machines, SVM, upper body pose, virtual reality

Automatic emotion recognition using computer vision is significant for many real-world applications like photojournalism, virtual reality, sign language recognition, and Human Robot Interaction (HRI) etc., Psychological research findings advocate that humans depend on the collective visual conduits of face and body to comprehend human emotional behaviour. Plethora of studies have been done to analyse human emotions using facial expressions, EEG signals and speech etc., Most of the work done was based on single modality. Our objective is to efficiently integrate emotions recognized from facial expressions and upper body pose of humans using images. Our work on bimodal emotion recognition provides the benefits of the accuracy of both the modalities.

Citation Keykeshari_emotion_2019