Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM

Abstract

Facial expression recognition (FER) plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning (DL) model using the kernel entropy component analysis network (KECANet) and directed acyclic graph support vector machine (DAGSVM). We use the KECANet in the feature extraction stage. In the stage of output, binary hashing and blockwise histograms are adopted. We sent the final output features to the DAGSVM classifier for expression recognition. We test the performance of our proposed method on three databases of CK+, JAFFE, and CMU Multi-PIE. According to the experiment results, the proposed method can learn high-level features and provide more recognition information in the stage of training, obtaining a higher recognition rate.

Global and local feature fusion via long and short-term memory mechanism for dance emotion recognition in robot

“…KECA works by projecting raw data into higher-dimensional space to Eigen decomposing the Kernel matrix (Chen et al, 2021b ). The eigenvector with the maximum eigenvalue is selected to form a new data space.…”