Normalized Co-Occurrence Mutual Information for Facial Pose Detection Inside Videos

Human faces captured inside videos are often presented with variable poses, making it difficult to recognize and thus pose detection becomes crucial for such face recognition under non-controlled environment. While existing mutual in formation (MI) primarily considers the relationship between corresponding individual pixels, we propose a normalized co occurrence mutual information in this letter to capture the information embedded not only in corresponding pixel values but also in their geographical locations. In comparison with the existing Mis, the proposed presents an essential advantage that both marginal entropy and joint entropy can be optimally exploited in measuring the similarity between two given images. When developed into a facial pose detection algorithm inside video sequences, we show, through extensive experiments, that such design is capable of achieving the best performances among all the representative existing techniques compared.