Automatic face recognition is an area with immense practical potential which includes a
wide range of commercial and law enforcement applications. Hence it is unsurprising that it …

Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …

Recognizing faces in unconstrained videos is a task of mounting importance. While
obviously related to face recognition in still images, it has its own unique characteristics and …

We propose a novel discriminative learning approach to image set classification by
modeling the image set with its natural second-order statistic, ie covariance matrix. Since …

Liver segmentation and recognition from computed tomography (CT) images is a warm topic
in image processing which is helpful for doctors and practitioners. Currently, many deep …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

In video based face recognition, great success has been made by representing videos as
linear subspaces, which typically lie in a special type of non-Euclidean space known as …

In this paper we propose a discriminant learning framework for problems in which data
consist of linear subspaces instead of vectors. By treating subspaces as basic elements, we …

We address the problem of comparing sets of images for object recognition, where the sets
may represent variations in an object's appearance due to changing camera pose and …

In this paper, we address the problem of classifying image sets, each of which contains
images belonging to the same class but covering large variations in, for instance, viewpoint …