Two-stage cascade model for unconstrained face detection

Abstract

In this paper, we propose a two-stage model for unconstrained face detection. The first stage is based on the normalized pixel difference (NPD) method, and the second stage uses the deformable part model (DPM) method. The NPD method applied to in the wild image datasets outputs the unbalanced ratio of false positive to false negative face detection when the main goal is to achieve minimal false negative face detection. In this case, false positive face detection is typically an order of magnitude higher. The result of the NPD-based detector is forwarded to the DPM-based detector in order to reduce the number of false positive detections. In this paper, we compare the results obtained by the NPD and DPM methods on the one hand, and the proposed two-stage model on the other. The preliminary experimental results on the Annotated Faces in the Wild (AFW) and the Face Detection Dataset and Benchmark (FDDB) show that the two-stage model significantly reduces false positive detections while simultaneously the number of false negative detections is increased by only a few.