Emotion Recognition by Two View SVM_2K Classifier on Dynamic Facial Expression Features

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

Advanced learning algorithms for cross-language patent retrieval and classification

Abstract We study several machine learning algorithms for cross-lan7guage patent retrieval and classification. In comparison with most of other studies involving machine learning for cross-language information retrieval, which basically used learning techniques for monolingual sub-tasks, our learning algorithms exploit the bilingual training documents and learn a semantic representation from them. We study Japanese-English cross-language patent retrieval using Kernel Canonical Correlation Analysis (KCCA), a method of correlating linear relationships between two variables in kernel defined feature spaces. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. We also investigate learning algorithms for cross-language document classification. The learning algorithm are based on KCCA and Support Vector Machines (SVM). In particular, we study two ways of combining the KCCA and SVM and found that one particular combination called SVM_2k achieved better results than other learning algorithms for either bilingual or monolingual test documents