Highly Efficient Face Detection in Color Images
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Efficient content analysis engine for visual surveillance network
IEEE Transactions on Circuits and Systems for Video Technology
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
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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. Index Terms-- Face Scoring, Neural Network, LMS, Face Detection, Video Object Segmentation.