LTP-ML: Micro-Expression Detection by Recognition of Local Temporal Pattern of Facial Movements

Abstract

The Micro-expressions (MEs) carry specific nonverbal information, for example the facial movement caused by pain. However, as a consequence of their local and short nature, it is difficult to detect MEs. This paper presents a novel detection method by recognizing a local and temporal pattern (LTP) of facial movement. In our system, with the purpose of improving the detection accuracy, temporal local features are generated from the video in a sliding window of 300ms (mean duration of a ME). These features are extracted from a projection in PCA space and form a specific pattern during ME which is the same for all MEs. Using a classical classification algorithm (SVM), MEs are then distinguished from other facial movements. Finally, a global fusion analysis is applied on the whole face to eliminate false positives. Experiments are performed on two databases: CASME I and CASME II. The detection results show that the proposed method outperforms the most popular detection method in terms of F1-score according to the analysis of multiple metrics.