Local Visual Primitives (LVP) for Face Modelling and Recognition

Xin Meng, Shiguang Shan, Xilin Chen, Wen Gao

Abstract: This paper proposes a novel simple yet effective generative model based on Local Visual Primitives (LVP) for face modeling and classification. The LVPs, as the pattern of local face region, are learnt by clustering a great number of local patches. Visually, these LVPs correspond to intuitive low-level micro visual structures very well, and they are expected to constitute those high-level semantic features, such as eyes, nose and mouth. We show that, though face appearances vary dramatically, these LVPs are very effective for face image reconstruction. For face recognition, block-based histograms of the LVPs indexes are extracted as the face representation to compare for classification. Primary experiments on FERET face database have shown that the LVP method can achieve encouraging recognition rate.