Jie Wang

TL;DR: A recognition framework based on the concept of the so-called generic learning is introduced as an attempt to boost the performance of traditional appearance-based recognition solutions in the one training sample application scenario.

TL;DR: The proposed scheme improves significantly the recognition performance of the eigenface solution and outperforms other state-of-the-art methods.

TL;DR: The proposed algorithm to optimize the Gaussian kernel parameters by maximizing a classical class separability criterion is solved through a quasi-Newton algorithm by making use of a recently proposed decomposition of the objective criterion.

TL;DR: A novel kernel-based QDA method is proposed to further relax the Gaussian assumption by using the kernel machine technique, and at the same time, tackles the so-called small sample size problem through a regularized estimation of the covariance matrix.

TL;DR: Experiments performed on real face databases indicate that the proposed kernel discriminant learning method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel principal component analysis and kernel linear discriminant analysis, at a significantly reduced computational cost.