Scale and Orientation Invariance in Human Face Detection

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 diierent scale and orientation. Feature points are detected from the image using spatial lters 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 eeciently with faces at diierent scale and orientation.