Combined Fisherfaces framework

摘要

In this paper, a Complex LDA based combined Fisherfaces framework, coined Complex Fisherfaces, is developed for face feature extraction and recognition. In this framework, Principal Component Analysis (PCA) and Kernel PCA (KPCA) are first used for feature extraction. Then, the resulting PCA-based linear features and KPCA-based nonlinear features are integrated by complex vectors and, Complex LDA is further employed for feature fusion. The proposed method is tested on a subset of FERET database. The experimental results demonstrate that Complex Fisherfaces outperforms Fisherfaces and Kernel Fisherfaces. Also, the complex vector based parallel feature fusion strategy is demonstrated to be much more effective and robust than the super-vector based serial feature fusion strategy for face recognition.