Static image facial expression recognition based on separability assessment of discrete separable shearlet transform

摘要

We present the discrete separable shearlet transform (DSST) separability assessment system for recognizing facial expressions. DSST is an image multiscale geometric analysis method. We use a separability assessment to evaluate the separability of different scales and directions of the coefficients after DSST transformation. First, all test and training images are normalized and equalized. Then, all preprocessed images are DSST-transformed, and all low-and high-frequency coefficients are obtained. Next, the separability of different scales and directions of the coefficients is evaluated, and we use only those that have a large separability index. Then, we combine the low-and high-frequency coefficients for the best separability direction and scale as the extracted features. Finally, we use a support vector machine to classify seven expressions (i.e., happiness, sadness, surprise, disgust, fear, anger, and neutrality) from the Japanese Female Facial Expression, Extended Cohn-Kanade, MMI, and Psychological Image Collection at Stirling datasets. The experimental results show that the recognition rate of the proposed method is better than those of state-of-the-art methods. (C) 2019 SPIE and IS&T