Selective part models for detecting partially occluded faces in the wild
Faces in the wild have recently captured the focus of researchers for all facial analysis problems. Partial occlusion is a major problem for analyzing faces captured in unconstrained non-cooperative conditions. Even detecting the faces in such conditions is a challenging problem that needs to be solved before any further analysis of such faces can be done. In this paper, we propose modelling the face as a collection of parts that can be selected from the visible regular facial features and some other objects that can possibly occlude faces such as sunglasses, caps and hands. The proposed algorithm is more toward scene understanding in the sense that it is not only detecting faces but it also suggests the visible parts of these faces and even some of the occluding objects which can help in any further analysis. Experimental results show a state of the art performance on the challenging FDDB database with a thorough analysis of the performance with different types of partial occlusion.