Video genre estimation from relationship between motion and facial features using SLPCCA

In this paper, we propose an efficient video genre estimation method based on the relationship between facial features and motion features. In the proposed method, we utilize supervised locality preserving canonical correlation analysis (SLPCCA), which is derived in the proposed method, to maximize the correlation between facial features and motion features. Moreover, by using SLPCCA, we can consider not only the correlation but also class information. Finally, by applying Support Vector Machine (SVM) to the SLPCCA-based feature vectors, we realize a successful video genre estimation. Experimental results show the effectiveness of our method.