A Novel Combined Fisherfaces Framework (TP-P2)

Author(s) : Wenshu Li (College of Computer Science and Technology, Zhejiang University, China) Changle Zhou (Department of Artificial Intelligence,Xiamen University, China)

Abstract : An improved LDA based combined personalized feature framework is proposed for face recognition (FR). It is well known that the distribution of face images is highly nonlinear under a large variation in viewpoints. Therefore, linear methods such as principle component analysis or linear discriminant analysis cannot provide reliable and robust solutions for FR problems. In our framework, the improved LDA makes use of the null space of the within-class scatter matrix effectively, and Global feature vectors and local feature vectors are integrated by complex vectors as input feature of improved LDA. The experiment results demonstrate that the proposed methodology is more effective and robust for face recognition with complex face variations.

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