Improving recognition accuracy for facial expressions using scattering wavelet




Facial expressions, Scattering waves, Deep learning, Gabor Philter, Recognition Rate


One of the most evident and meaningful feedback about people’s emotions is through facial expressions. Facial expression recognition is helpful in social networks, marketing, and intelligent education systems. The use of Deep Learning based methods in facial expression identification is widespread, but challenges such as computational complexity and low recognition rate plague these methods. Scatter Wavelet is a type of Deep Learning that extracts features from Gabor filters in a structure similar to convolutional neural networks. This paper presents a new facial expression recognition method based on wavelet scattering that identifies six states: anger, disgust, fear, happiness, sadness, and surprise. The proposed method is simulated using the JAFFE and CK+ databases. The recognition rate of the proposed method is 99.7%, which indicates the superiority of the proposed method in recognizing facial expressions.


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How to Cite

M. Davari, A. Harooni, A. Nasr, K. Savoji, and M. Soleimani, “Improving recognition accuracy for facial expressions using scattering wavelet”, EAI Endorsed Trans AI Robotics, vol. 3, Mar. 2024.