BRIEF-based face descriptor: an application to automatic facial expression recognition (AFER)

Abstract

This paper presents a new face descriptor based on binary robust independent elementary features (BRIEF) (Calonder et al. in IEEE Trans Pattern Anal Mach Intell 34(7):1281–1298, 2012; in: European conference on computer vision, Springer, pp 778–792, 2010). The most important properties of BRIEF are the independence of the descriptor length from the matching window size and the possibility of using a subsample of pixel pairs located at arbitrary positions in the matching window. Furthermore, BRIEF is computationally simple and gives a compact representation. The BRIEF descriptor can be used to generate discriminative features globally from an image. However, when BRIEF is used to generate features from a region of an image with no explicit shape such as a face in an image, BRIEF must be used locally to ensure that each pixel in the region is evaluated locally to capture the local properties. In this paper, the performance of BRIEF feature is evaluated in the task of AFER. Using three different facial expression databases, we demonstrate that BRIEF provides satisfactory, encouraging and comparable performance to the performance of alternative face representation tested on the same data, and it is efficient in terms of memory usage and execution time.