Histogram-based local descriptors for facial expression recognition (FER): A comprehensive study

This paper aims to present histogram-based local descriptors applied to Facial Expression Recognition (FER) from static images and provide a systematic review and analysis of them. First, we describe the main steps in encoding binary patterns in a local patch, which are required in every histogram-based local descriptor. Then, we list the existing local descriptors, while analysing their strengths and weaknesses. Finally, we present the experimental results of all these descriptors on commonly used facial expression databases, with varying resolution, noise, occlusion, and number of sub-regions, as well as comparing them with the results obtained by the state-of-the-art deep learning methods. This paper aims to bring together different studies of the visual features for FER by evaluating their performances under the same experimental setup, and critically reviewing various classifiers making use of the local descriptors.