Low-resolution facial expression recognition: A filter learning perspective

Abstract(#br)Automatic facial expression recognition has attracted increasing attention for a variety of applications. However, the problem of low-resolution generally causes the performance degradation of facial expression recognition methods under real-life environments. In this paper, we propose to perform low-resolution facial expression recognition from the filter learning perspective. More specifically, a novel image filter based subspace learning (IFSL) method is developed to derive an effective facial image representation. The proposed IFSL method mainly includes three steps: Firstly, we embed the image filter learning into the optimization process of linear discriminant analysis (LDA). By optimizing the cost function of LDA, a set of discriminative image filters (DIFs) corresponding to different facial expressions is learned. Secondly, the images filtered by the learned DIFs are added together to generate the combined images. Finally, a regression learning technique is leveraged for subspace learning, where an expression-aware transformation matrix is obtained using the combined images. Based on the transformation matrix, IFSL effectively removes irrelevant information while preserving useful information in the facial images. Experimental results on several facial expression datasets, including CK+, MMI, JAFFE, SFEW and RAF-DB, show the superior performance of the proposed IFSL method for low-resolution facial expression recognition, compared with several state-of-the-art methods