Rejecting pseudo-faces using the likelihood of facial features and skin
Face detection and Facial feature extraction are considered among the most studied topics in the field of biometrics. In real-world uncontrolled scenarios, high rate of false alarm is still a major problem. This paper presents a solution to reduce false alarm rate resulting from any generic face detector, through a fast post-processing algorithm based on utilizing a probabilistic framework for facial feature extraction combined with skin model. In particular, the facial features detection method inspects the geometry of the face and the likelihood of each facial feature location while the skin detector utilizes the complimentary information of color. Experimental results show a significant improvement over the state-of-the-art on the FDDB and LFPW database.