|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
SCALE AND ORIENTATION INVARIANCE IN HUMAN FACE DETECTION
Kin Choong Yow and Roberto Cipolla
Many current human face detection algorithms make implicit assumptions about the scale, orientation or viewpoint of faces in an image and exploit these constraints to detect and localize faces. The algorithm may be robust for the assumed conditions but however it becomes very difficult to extend the results to general imaging conditions. In an earlier paper, we proposed a feature-based face detection algorithm to detect faces in complex background. In this paper, we will examine its ability to detect faces under different scale, orientation and viewpoint. The results show that the algorithm can indeed cope with a good range of scale, orientation and viewpoint variations that is typical of a subject sitting in front of a computer terminal.
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