Abstract
Facial expressions play an important role in identifying the emotional state of an individual. Individuals can have different reactions to the same stimuli. Various emotions (anger, disgust, fear, joy, sadness, surprise or neutral) are detected while the user plays a game and their facial expressions are analyzed through a live web camera. Implementing the Haar Algorithm, the frames are cropped and the face alone is procured on which grey scaling and resizing process is carried out. Now the most necessary features of the face are extracted by a neural network model which will encode motion and facial expressions to predict emotion. In this paper, a linear model along with ResNeXt and PyramidNet models are analyzed side by side to identify the algorithm which gives the best accuracy.
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Acknowledgement
We express our sincere thanks to PSG College of Technology for providing constant support and guidance throughout the project. We also acknowledge Google for providing a Colab platform for training and testing the Neural Network models for facial emotion recognition using deep learning.
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Kumar, B.V., Jayavarshini, R., Sakthivel, N., Karthiga, A., Narmadha, R., Saranya, M. (2022). Evaluation of Deep Architectures for Facial Emotion Recognition. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1567. Springer, Cham. https://doi.org/10.1007/978-3-031-11346-8_47
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