Automatic Feature-Based Face Scoring in Surveillance Systems

Facial images with low resolution in surveillance sequences are hard to detect with traditional approaches, and the quality of these faces is a significant factor for human face recognition. A new technique called face scoring, which determines the face scores based on face quality, is proposed. It combines spirits of image-based face detection and essences of video object segmentation to filter out face candidates. Besides, the face scoring technique includes eight scoring functions based on feature extraction technique, integrated by a single layer neural network training system to obtain an optimal linear combination to select high-quality faces. In the proposed algorithm, the way to choose input vector is quite different from traditional approaches and has good properties. Experiments show that the proposed algorithm effectively extracts low-resolution human faces, which traditional algorithm cannot handle well. It can also rank face candidates according to face scores, which is useful for surveillance video summary and indexing.