Improving the Accuracy of Face Detection for Damaged Video and Distant Targets
Jun-Horng Chen · 2014
This work aims at improving the accuracy of face detection in two scenarios, when the video quality is deteriorated by the transmission link and when the target is far away from the camera. In block based coding, the packet loss inevitably makes the corrupted face image lacks some blocks. This work proposes the sparse modeling error concealment can coarsely recover the lost blocks, the fine texture can be obtained by diminishing the edge discontinuity, and a satisfied result for face detection can thus be recovered. Furthermore, this work utilizes the relationship learning super-resolution method to enhance the resolution in the case of face image taken from a long distance. Experimental results demonstrate that the proposed approach can effectively increase the accuracy of face detection for severely degraded and low resolution face images.