Robust nose tip localization based on two-stage subclass discriminant analysis

Nose is one of the salient features in a human face, and its localization is important for face recognition, face pose recognition, 3D face reconstruction, etc. In this paper, a novel nose tip localization method is proposed, which is based on two-stage subclass discriminant analysis (SDA). At the first stage, some randomly selected image patches are used as negative samples for the training of SDA classifier, and nose is detected from the whole face image. The second stage refines nose tip position by using some nose context patches as negative samples for the training of SDA classifier. The proposed method detects nose from the whole face image and no a priori knowledge about the layout of face components is used. Experimental results on AR images show that the proposed method can achieve high nose tip localization rates, and is robust to changes of illumination and facial expression.