Occlusion Invariant Face Recognition Using Selective LNMF Basis Images

In this paper, we propose a novel occlusion invariant face recognition algorithm based on Selective Local Nonnegative Matrix Factorization (S-LNMF) technique. The proposed algorithm is composed of two phases; the occlusion detection phase and the selective LNMF-based recognition phase. We use local approach to effectively detect partial occlusion in the input face image. A face image is first divided into a finite number of disjointed local patches, and then each patch is represented by PCA (Principal Component Analysis), obtained by corresponding occlusion-free patches of training images. And 1-NN threshold classifier was used for occlusion detection for each patch in the corresponding PCA space. In the recognition phase, by employing the LNMF-based face representation, we exclusively use the LNMF bases of occlusion-free image patches for face recognition. Euclidean nearest neighbor rule is applied for the matching. Experimental results demonstrate that the proposed local patch-based ...

• Type: Conference
• Year: 2006
• Where: ACCV
• Authors: Hyun Jun Oh, Kyoung Mu Lee, Sang Uk Lee, Chung-Hyuk Yim
• Keywords: ACCV 2006, Computer Vision, Face Recognition, Occlusion Detection, Occlusion Invariant Face