Abstract
A novel emotion recognition system has been proposed for classifying facial expression in videos. Firstly, two types of basic facial appearance descriptors were extracted. The first type of descriptor, called Motion History Histogram (MHH), was used to detect temporal changes of each pixels of the face. The second type of descriptor, called Histogram of Local Binary Patterns (LBP), was applied to each frame of the video and was used to capture local textural patterns. Secondly, based on these two basic types of descriptors, two new dynamic facial expression features called MHH_EOH and LBP MCF were proposed. These two features incorporate both dynamic and local information. Finally, the Two View SVK_2K classifier was built to integrate these two dynamic features in an efficient way. The experimental results showed that this method outperformed the baseline results set by the FERA'11 challenge.