Incremental two-dimensional two-directional principal component analysis (I(2D)2PCA) for face recognition

In this paper, we propose a new incremental two-directional two-dimensional principal component analysis (I(2D) 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) 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.