Robust Speaking Face Identification for Video Analysis

We investigate the problem of automatically identifying speaking faces for video analysis using only the visual information. Intuitively, mouth should be first accurately located in each face, but this is extremely challenging due to the complicated condition in video, such as irregular lighting, changing face poses and low resolution etc. Even though we get the accurate mouth location, it's still very hard to align corresponding mouths. However, we demonstrate that high precision can be achieved by aligning mouths through face matching, which needs no accurate mouth location. The principal novelties that we introduce are: (i) proposing a framework for speaking face identification for video analysis; (ii) detecting the change of the aligned mouth through face matching; (iii) introducing a novel descriptor to describe the change of the mouth. Experimental results on videos demonstrated that the proposed approach is efficient and robust for speaking face identification.