Towards Micro-expression Recognition Through Pyramid of Uniform Temporal Local Binary Pattern Features
Compared to macro-expressions, recognizing micro-expres-sions is more challenging due to low intensity and their brief duration. To deal with this issue, the present paper proposes a facial micro-expression recognition approach based on the pyramid of uniform Temporal Local Binary Pattern (PTLBP\(^{u2}\)) features for describing the appearance motion changes in time through video stream. Unlike the majority of approaches that use a high dimensional feature space, the proposed approach is based on a low dimensional space with only 83 features. Compared to the most recent facial micro-expression recognition approaches, our approach proves its effectiveness with an accuracy rate reaching 66.40% on Casme II dataset. A study of the ability of a macro-expression model to recognize micro-expression shows that it is more efficient to recognize certain micro-expressions than others.
In this article a construction of hybrid colour space from a set of classic colours spaces by using the algorithm MRMR is proposed. This algorithm is based on the mutual information. Our approach is evaluated on SPOT HRV (XS) image representing two forest areas in the region of Rabat. Feature extraction is done by the coooccurrence matrix. The SVM (Support Vectors Machine) classifier is used.