Biased discriminant analysis using composite vectors for eye detection

We propose a new discriminant analysis using composite vectors for eye detection. A composite vector consists of a number of pixels inside a window on an image. The covariance of composite vectors is obtained from their inner product and can be considered as a generalized form of the covariance of pixels. The proposed C-BDA is a biased discriminant analysis using the covariance of composite vectors. In the hybrid cascade detector constructed for eye detection, Haar-like features are used in the earlier stages and composite features obtained from C-BDA are used in the later stages. The experimental results for the CMU and Yale databases show that the proposed detector provides robust performance to several kinds of variations such as facial pose, illumination, and closed eyes. In particular, it provides a 99.4% detection rate for the CMU images without glasses.