| Summary: | 碩士 === 元智大學 === 資訊工程學系 === 99 === Automatic recognition of human faces is a preliminary task in many applications such as security checks, human interaction systems, and face collection management. On the other hand, the prerequisite for a successful face recognition system is to detect faces in a given unknown picture. However, the task of automatic face detection in a complex background is difficult to cope with in particular for occluded or rotated faces, lighting distortions and non-uniform illumination. In this thesis, a face detector is proposed to resolve the problem.
Our method consists of two phases: classification and verification. In the classification phase, a classifier using local block edge characteristics and support vector machine is first proposed. Sliding window approach using the proposed face classifier is then adopted to search multi-scale face candidates followed by mean-shift method to reduce duplicate face candidates so as to locate face candidates accurately. In the verification phase, 2-means algorithm is invoked locate face candidate region more accurately. The face classifier can then applied again to confirm whether a face exists. In this way the error of false alarm can be reduced. Various experiments prove the feasibility and effectiveness of the proposed method.
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