Face detection model based on distance measure of regional feature

This paper proposes a new model for face detection based on statistics by applying the mosaic method. The model is insensitive to illumination and can adapt to the distortion and slope of face in some degrees. Moreover, there is an extra good feature of this model, i.e., an attention focusing capability. First, a distance measure, that is the Mahalanobis distance (MD), is presented, which is suitable for vector clustering. Next, the standard deviation of gradient norm and direction angle in each mosaic block are selected as the regional features in the model. The tested images are detected as human faces when both the MDs of the gradient norm and the direction angle to the collectivity of human faces are small. Finally, some experiments were made under different conditions and the results obtained show the model performed as expected.