Face Recognition using Layered Linear Discriminant Analysis and Small Subspace

Face recognition has great demands in human recognition and recently it becomes one of the most important research areas of biometrics. In this paper, we present a novel layered face recognition method based on Fisher's linear discriminant analysis. The basic aim is to decrease FAR by reducing the face dataset to small size by applying layered linear discriminant analysis. Although, the computational complexity at the time of recognition is much higher than conventional PCA and LDA due to the weights computation for small subspace at the time of recognition, but on the other hand the layered LDA provides significant performance gain especially on similar face database. Layered LDA is insensitive to large dataset and also small sample size and it provides 93% accuracy on BANCA face database. Experimental and simulation results show that the proposed scheme has encouraging results for a practical face recognition system.