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

Highlights • This paper provides a systematic review of current histogram-based local feature descriptors, which have been applied for facial-expression recognition. • The weaknesses and strengths of the existing descriptors are discussed and analysed. • A comprehensive evaluation of the performances of 27 local descriptors is presented. • The best overall performances are obtained by LPQ and LGBPHS. 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. [ABSTRACT FROM AUTHOR]