Face Detection and Extraction from Low Resolution Surveillance Video Using Motion Segmentation

Face detection is a prominent research domain in the field of digital image processing particularly in the field of video surveillance systems. Today is the world of video technology starting from low resolution videos to the high definition videos. The videos obtained from surveillance systems are often of low resolution due to the reasons such as distance between the camera and place of footage, environment factors, wide coverage area, installation problems, out of focus, bandwidth issue, hardware constraints, storage space limitations etc. because of which the frames need to be compressed or converted to lower resolution before storage. In this paper, we have worked on motion segmentation based face detection from low resolution surveillance videos. The motion segmentation is used to extract the region of interest from the current frame. Thereafter only the pixels obtained after the motion segmentation are subjected to the face detection process. The haar features based face detection has been used in this work, employing the image scaling to facilitate multi-scale face detection. Considerable search space reduction and efficiency boost has been achieved by proposed motion segmentation technique.