Face Detection and Camouflage Breaking by Direct Convexity Estimation
Edge detection was, so far, the core of most state of the art techniques for attentional mechanisms as well as face detection.1,2 This excludes some recent works which utilize neural networks,3,4 colour histograms,5,6 or shape statistics7,8 for face detection. Though one cannot disregard their advantages, edge maps sustain severe flaws such as: sensitivity to changes in illumination, strong effect of surrounding objects, and inability to delineate objects in a cluttered scene. We overcome these problems of edge-based approaches by a novel attentional operator which detects smooth three dimensional convex or concave objects in the image. The operator is robust to face orientation, scale, and illumination, and is capable of detecting the subject in a strongly textured background. It is employed for face detection, namely to detect the eyes and hair, from which the scale of the face can be inferred. The operator answers the above problems as a whole, demands a relatively short running time, and its robustness leads to reliable results.