547 research outputs found
Staticand Dynamic Facial Emotion Recognition Using Neural Network Models
Emotion recognition is the process of identifying human emotions. It is made
possible by processing various modalities including facial expressions, speech signals,
biometricsignals,etc. Withtheadvancementsincomputingtechnologies,FacialEmo
tion Recognition (FER) became important for several applications in which the userās
emotional state is required, such as emotional training for autistic children. The recent
years witnessed a major leap in Artiļ¬cial Intelligence(AI),specially neural networks for
computer vision applications. In this thesis, we investigate the application of AI algo
rithms for FER from static and dynamic data. Our experiments address the limitations
and challenges of previous works such as limited generalizability due to the datasets.
We compare the performance of machine learning classiļ¬ers and convolution neural
networks (CNNs) for FER from static data (images). Moreover, we study the perfor
mance of the proposed CNN for dynamic FER(videos),in addition to Long-ShortTerm
Memory(LSTM)inaCNN-LSTM hybrid approach to utilize the temporal information
in the videos. The proposed CNN architecture out performed the other classiļ¬ers with an
accuracy of 86.5%. It also outperformed the hybrid approach for dynamic FER which
achievedanaccuracyof74.6
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