An Enhanced Expression Recognition Based on Sparse Representation Using Data Normalization
Xiaofang Jin, Yijia Xiao · 2021
As a hot issue of visual recognition and the premise of human-computer intelligent interaction, emotion recognition from facial expression is highly recognized and widely used. However, face images collected in the natural environment tend to have different head postures and intensities of illumination, which interfere with the discrimination effect of the facial expression. In this paper, an enhanced facial expression recognition model based on sparse representation using data normalization is designed, which can boost the recognition performance in an unconstrained environment compared with the original model. Multi-task Convolutional Neural Networks (MTCNN) is used for facial landmark detection along with affine transformation for aligning all the faces. Meanwhile, histogram equalization is employed to normalize the gray-scale. Finally, the test image is classified by sparse coding. The real effect of the proposed model is verified by experiments on the CK+ and Oulu-CASIA database, and the accuracy is improved to 51.33% and 60.76% respectively. Besides, the amount of data is greatly reduced by sparse coding.