Face validation using skin, eyes and mouth detection
In unconstrained facial images, large visual variations such as those due to pose, scale, presence of occlusions, expressions and lighting cause difficulties in discriminating faces from the background accurately, so as a result, there are non-face regions that are recognized as faces (false positive), whereas the effectiveness of face detection algorithms is characterized by low false positive (FP) rate, high detection rate and high speed of processing. So, to reduce these non-face regions, instead of developing accurate face detection algorithm that needs much time for processing, face validation step will be added after the detection. In this paper, new fast face validation method is proposed. It consists of two steps, the first one is skin detection using YCbCr color method. The second step is eyes and mouth detection using Cascading approach; In this step, region of candidate face is divided into two overlapping regions, one for the eye detection model and the other for mouth detection model. For evaluation our method, SVM face detection algorithm is used as a baseline validation algorithm. The experimental results on FDDB dataset showed a better performance of our proposed method (2 ms validation time compared to 500 ms in the SVM algorithm) and a similar number of rejected FP.