Facial Micro-Expression Recognition using Feature Extraction

IJCSEC Front Page

Micro-expressions are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying small expressions is effective as recognizing them has several vital applications, notably in forensic science and psychotherapy. However, analyzing spontaneous Micro expressions is very challenging due to their short duration and low intensity. Nowadays, Video-based facial expression recognition has received significant attention because of its wide spread applications. One key issue for video-based facial expression analysis in practice is how to extract dynamic features. This work proposes a novel approach using histogram sequence of local binary patterns from three orthogonal planes (LBP-TOP). In this approach, every facial image sequence is firstly convolved with Gabor filters to extract the Gabor Magnitude Sequences (GMSs) that extract local information of magnitude, phase and orientation. The main Facial action units which are located at eye and mouth regions of face are extracted and apply feature extraction to increase the discrimination for micro-expressions. For classification nearest neighbour method is exploited. Our experimental results on CASME2 database demonstrate that proposed method achieved better results compared to other methods in past years.

Keywords: Micro-Expressions, Gabor filters, LBP-TOP, Nearest Neighbour method.


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