Scoring recognizability of faces for security applications

In security applications the human face plays a fundamental role, however we have to assume non-collaborative subjects. A face can be partially visible or occluded due to common-use accessories such as sunglasses, hats, scarves and so on. Also the posture of the head influence the face recognizability. Given a video sequence in input, the proposed system is able to establish if a face is depicted in a frame, and to determine its degree of recognizability in terms of clearly visible facial features. The system implements features filtering scheme combined with a skin-based face detection to improve its the robustness to false positives and cartoon-like faces. Moreover the system takes into account the recognizability trend over a customizable sliding time window to allow a high level analysis of the subject behaviour. The recognizability criteria can be tuned for each specific application. We evaluate our system both in qualitative and quantitative terms, using a data set of manually annotated videos. Experimental results confirm the effectiveness of the proposed system.