Face detection and recognition using PCA
S. H. Lee, Sang-Bong Jung, Jang-Woo Kwon, Seung-Hong Hong · 2003
In this paper we developed a computer system that can locate and track a subject's head in a complex background and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by color and motion Information and PCA (principal component analysis). Our approach treats the face recognition problem as a two-dimensional (2-D) problem rather than three-dimensional geometry. So, the problem is easier to treat. The system functions by two steps, first, extracting face image in a complex background using difference image and color model, and second, projecting pre-extracted face images onto a feature space that represents the significant variations among known face images. We use this weight vector to recognize each individual. Several evaluation methods of this weight vector are attempted in this paper.