In the field of face recognition it is generally believed that ”state of the art” recognition rates can only be achieved when discriminative (e.g., linear or generalized discriminant analysis) rather than expressive (e.g., principal or kernel principal component analysis) methods are used for facial feature extraction. However, while being superior in terms of the recognition rates, the discrim...

We propose a general approach to discriminant feature extraction and fusion, built on an optimal feature transformation for discriminant analysis [6]. Our experiments indicate that our approach can dramatically reduce the dimensionality of original feature space whilst improving its discriminant power. Our feature fusion method can be carried out in the reduced lowerdimensional subspace, result...

In practice, the data distribution at test time often differs, to a smaller or larger extent, from that of original training data. Consequentially, so-called source classifier, trained on available labelled data, deteriorates test, target, Domain adaptive classifiers aim combat this problem, but typically assume some particular form domain shift. Most are not robust violations shift assumptions...

In this paper, we present a novel multilinear algebra based feature extraction approach for face recognition which preserves some implicit structural or locally-spatial information among elements of the original images. We call this method three-dimensional modular discriminant analysis (3DMDA). Our approach uses a new data model called third-order tensor model (3TM) for representing the face i...

In practical applications, we often have to deal with high order data, such as a grayscale image and a video sequence are intrinsically 2nd-order tensor and 3rd-order tensor, respectively. For doing clustering or classification of these high order data, it is a conventional way to vectorize these data before hand, as PCA or FDA does, which often induce the curse of dimensionality problem. For t...