This paper addresses the problem of face recognition using independent component analysis (ICA). More specifically, we are going to address two issues on face representation using ICA. First, as the independent components (ICs) are independent but not orthogonal, images outside a training set cannot be projected into these basis functions directly. In this paper, we propose a least-squares solution method using Householder Transformation to find a new representation. Second, we demonstrate that not all ICs are useful for recognition. Along this direction, we design and develop an IC selection algorithm to find a subset of ICs for recognition. Three public available databases, namely, MIT Al Laboratory, Yale University and Olivette Research Laboratory, are selected to evaluate the performance and the results are encouraging.
Scopus Subject Areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence
- Face recognition
- Independent component analysis
- Principal component analysis