Composite Binary Pattern Assisted Micro-Expressions Spotting Through Feature Difference Analysis

Abstract

Micro-Expressions (MEs) are one kind of facial movement which is very spontaneous and involuntary in nature. MEs are observed when a person attempts to hide or conceal the experiencing emotion in a high-stakes environment. The duration of ME is very short and approximately less than 500 milliseconds. Recognition of such kinds of expressions from lengthy video consequences to a limited Micro Expression Recognition Performance and also creates the computational burden. Hence, in this paper, we propose a new ME spotting (detection of ME frames) method based on a new texture descriptor called Composite Binary Pattern (CBP). As a pre-processing, we employ the viola jones algorithm for landmark regions detection followed by landmark points detection for facial alignment. Next, every aligned face is described through CBP and subjected to feature difference analysis followed by the threshold for ME spotting. For simulation, the REVIEW dataset is used and the performance is measured through Recall, Precision, and F-Score.