Efficient face recognition based on MCT and I(2D)2PCA
Biho Kim, Hyung‐Min Park · 2012
This paper presents a robust algorithm to recognize human faces efficiently. Although the principle component analysis (PCA) is one of the most popular feature extraction methods, it requires too much computational load and memory capacity to implement a real-time embedded system for face recognition. To overcome the drawback, we employ the incremental two-directional two-dimensional PCA (I(2D)2PCA) which combines (2D)2PCA to demand much less computational complexity than the conventional PCA and the incremental PCA (IPCA) to adapt the eigenspace only using a new incoming sample datum without memorizing all of the previous trained data. In addition, robustness to illumination variations is addressed by introducing the modified census transform (MCT) which is a local normalization method useful for real-world application and implementation in an embedded system. Experimental results on the Yale Face Database B demonstrate that the proposed method based on the I(2D)2PCA with MCT preprocessing provided efficient and robust face recognition.