Face Detection Based On Curvelet Transform

Research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition, face detection and location is involved in all of these researching. Face detection has been a research focus of computer vision and pattern recognition.Curvelet transform is one of the multiscale transforms which are implemented in recent years. It contains more directional information, can represent the edge use less coefficients, it has optimally sparse representation of objects with edges, in other words this transform can represent the eigenvalue of the image. The digital algorithm of the first generation curvelet transform is redundant. Now, the digital algorithm of the second generation curvelet transform, namely, fast discrete curvelet transform, has been implemented.A face detector based on skin color segmenting preprocessing and curvelet transform is designed in this thesis. First, we designed a skin color segmenting approach based on YCbCr color space. Applied this approach to the preprocessing of the face detection system, it can discard background regions of the image quickly, so enhance the executing efficiency and detecting performance of the face detection system. Secondly, face feature extraction algorithm based on curvelet transform is presented, so we can represent eigenface with the coefficients of curvelet transform. Finally, take curvelet transform on every face color region which is preprocessed, then get the result of face detection through comparing the coefficients of coarse scale with the eigenface,The results of experiments show that the detection methods proposed in this paper have excellent performances and high practicality.