Abstract
Facial expression recognition is an important technology in human-computer interaction. This study investigates a method for facial expression recognition using quaternion neural networks. A multi-layer quaternion neural network that conducts its learning using a quaternion back-propagation algorithm is employed to design the facial expression recognition system. The input feature vector of the recognition system is composed of histograms of oriented gradients calculated from an input facial expression image, and the output vector of the quaternion neural network indicates the class of facial expressions such as happiness, anger, sadness, fear, disgust, surprise and neutral. Computational experimental results show the feasibility of the proposed method for recognising human facial expressions.
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Takahashi, K., Takahashi, S., Cui, Y., Hashimoto, M. (2014). Remarks on Computational Facial Expression Recognition from HOG Features Using Quaternion Multi-layer Neural Network. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_2
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DOI: https://doi.org/10.1007/978-3-319-11071-4_2
Publisher Name: Springer, Cham
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