Abstract
In recent days, automatic emotion detection is a field of interest and is used in fields such as e-learning, robotic applications, human–computer interaction (HCI), surveillance, ATM monitoring, mood-based playlists/YouTube videos, psychological studies, medical fields like supporting blind and dumb people, for treating autism in children, entertainment, animation, etc., The proposed work describes detection of human emotions from a real-time video or image with the help of classification technique. The major part of human communication constitutes of facial expression, which is around 55% of the total communicated information. The basic facial expressions that are considered by the psychologists are: happiness, sadness, anger, fear, surprise, disgust, and neutral. The proposed work aims to classify a given video into one of the above emotions using efficient facial features extraction techniques and SVM classifier. The author’s contribution is to increase the efficiency in emotion recognition by implementing the above mentioned superior feature extraction and classification methods.
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Acknowledgment
We would like to thank our class adviser Mr. Gandhiraj R for guiding us through-out the project. We also thank the university and lab staff for providing us with the software and all other necessities. We would like to thank our friends and college department for supporting us for completing the project successfully.
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Tamil Selvi, P., Vyshnavi, P., Jagadish, R., Srikumar, S., Veni, S. (2017). Emotion Recognition from Videos Using Facial Expressions. In: Dash, S., Vijayakumar, K., Panigrahi, B., Das, S. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 517. Springer, Singapore. https://doi.org/10.1007/978-981-10-3174-8_47
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DOI: https://doi.org/10.1007/978-981-10-3174-8_47
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