  Automatic facial expression analysis is a challenging issue and influenced so
many areas such as human computer interaction. Due to the uncertainties of the
light intensity and light direction, the face gray shades are uneven and the
expression recognition rate under simple Local Binary Pattern is not ideal and
promising. In this paper we propose two state-of-the-art descriptors for
person-independent facial expression recognition. First the face regions of the
whole images in a video sequence are modeled with Volume Local Directional
Binary pattern (VLDBP), which is an extended version of the LDBP operator,
incorporating movement and appearance together. To make the survey
computationally simple and easy to expand, only the co-occurrences of the Local
Directional Binary Pattern on three orthogonal planes (LDBP-TOP) are debated.
After extracting the feature vectors the K-Nearest Neighbor classifier was used
to recognize the expressions. The proposed methods are applied to the videos of
the Extended Cohn-Kanade database (CK+) and the experimental outcomes
demonstrate that the offered techniques achieve more accuracy in comparison
with the classic and traditional algorithms.
