Efficient Face Detection with Segmentation and Feature-based Face Scoring in Surveillance Systems

Facial images with low resolution in surveillance sequences are hard to detect with traditional approaches. An efficient face detection and face scoring technique in surveillance systems is proposed. It combines spirits of image-based face detection and essences of video object segmentation to filter out high-quality faces. The proposed face scoring technique, which is useful for surveillance video summary and indexing, includes four scoring functions based on feature extraction and is integrated by a neural network training system to select high-quality face. Experiments show that the proposed algorithm effectively extracts low-resolution human faces, which traditional face detection algorithm cannot handle well. It can also rank face candidates according to face scores, which determine face quality.