Eigenspace updating for non-stationary process and its application to face recognition

In this paper, we introduce a novel approach to modeling non-stationary random processes. Given a set of training samples sequentially, we can iteratively update the eigenspace to manifest the current statistics provided by each new sample. The updated eigenspace is derived based more on recent samples and less on older samples, controlled by a number of decay parameters. Extensive study has been performed on how to choose these decay parameters. Other existing eigenspace updating algorithms can be regarded as special cases of our algorithm. We show the effectiveness of the proposed algorithm with both synthetic data and practical applications on face recognition. Significant improvements have been observed on face images with different variations, such as pose, expression and illumination variations. We expect the proposed algorithm to have other applications in active recognition and modeling as well.