A Novel Algorithm for Rotated Human Face Detection

Xiao-guang Lv, Jie Zhou, Changshui Zhang

Abstract: We present a framework to detect human faces rotated within the image plane. A novel orientation histogram of an image is constructed using local orientation analysis. From the view of an oriented pattern, a histogram of the human face shows symmetry with respect to the orientation, /spl beta/, of the principal axis, and a local peak also occurs at the orientation orthogonal to /spl beta/. We analyze the histogram to find the orientation of the face principal axis, with which conventional upright face detectors can be used to verify the supposed rotated face. Experimental results show that the algorithm is effective.