An Unconstrained Face Detection Algorithm Based on Visual Saliency

This paper a novel face detection method based on visual saliency mechanism to improve the accuracy of unconstrained face recognition. Log Gabor transformation is used to extract visual features, and obtain facial saliency map by using stable balance measurement method based on Graph-Based Visual Saliency. Then binary image is obtained by segmenting facial saliency map with maximum entropy threshold and the rectangle area is marked by setting the centroid of object region as the center. Face region is detected from the original image according to the rectangle area. Experimental results on LFW database show that our algorithm can effectively remove the background interference without losing any face information and quickly precisely detect the face region which is more conducive to the unconstrained face recognition.