SCALE AND ORIENTATION INVARIANCE IN HUMAN FACE DETECTION
Kin Choong Yow and Roberto Cipolla
Human face detection has always been an important problem for face, expression and gesture recognition. Though numerous attempts have been made to detect and localize faces, these approaches have made assumptions that restrict their extension to more general cases. In this research, we propose a feature-based face detection algorithm that can be easily extended to detect faces under different scale and orientation. Feature points are detected from the image using spatial filters and grouped into face candidates using geometric and gray level constraints. A probabilistic framework is then used to evaluate the likelihood of the candidate as a face. We provide results to support the validity of the approach, and show that the algorithm can indeed cope efficiently with faces at different scale and orientation.
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