In this paper, we propose a new incremental two-directional two-dimensional principal component analysis (I(2D) 2 PCA) to efficiently recognize human faces. For implementing a real time face recognition system in an embedded system, the reduction of computational load as well as memory of a feature extraction algorithm is very important issue. The (2D) 2 PCA is faster than the conventional PCA. From memory capacity point of view, the incremental PCA is very efficient algorithm by adapting the eigensapce only using a new incoming sample data without memorizing all of previous trained data. In order to construct an efficient algorithm with less memory and small computational load, we propose a new feature extraction method by combining the IPCA and the (2D) 2 PCA. To evaluate the performance of the proposed I(2D) 2 PCA, a series of experiments were performed on two face image databases: ORL and Yale face databases. The experimental results show that the proposed feature extraction method is efficient by reducing the memory while computational load is nearly similar to (2D) 2 PCA.
['Yonghwa Choi', 'Takaomi Tokumoto', 'Minho Lee', 'Seiichi Ozawa']
Incremental two-dimensional two-directional principal component analysis (I(2D) 2 PCA) for face recognition