Probabilistic modeling is the most successful approach widely used in speaker recognition either for modeling the speakers in GMM-UBM structure or by serving as a prior in secondarylevel feature extraction to form i-vectors. In this paper, we introduce exemplar-based sparse representation and sparse discrimination for closed-set speaker identification in a noisy living room from very short spee...

This paper proposes a novel latent semantic learning method for extracting high-level latent semantics from a large vocabulary of abundant mid-level features (i.e. visual keywords) with structured sparse representation, which can help to bridge the semantic gap in the challenging task of human action recognition. To discover the manifold structure of mid-level features, we develop a graph-based...

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...

Iris recognition is one of the most reliable methods for identification. In general, itconsists of image acquisition, iris segmentation, feature extraction and matching. Among them, iris segmentation has an important role on the performance of any iris recognition system. Eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. In thi...

Recently, patch-based segmentation has been proposed for brain MR images. However, the segmentation accuracy of this method depends on similarities over small image patches, which may not be an optimal estimator. In this paper, we propose a new segmentation strategy based on patch reconstruction rather than patch similarity. In the proposed method, the training patch library is considered as a ...

A variety of real-world tasks involve the classification of images into pre-determined categories. Designing image classification algorithms that exhibit robustness to acquisition noise and image distortions, particularly when the available training data are insufficient to learn accurate models, is a significant challenge. This dissertation explores the development of discriminative models for...

iris recognition is one of the most reliable methods for identification. in general, itconsists of image acquisition, iris segmentation, feature extraction and matching. among them, iris segmentation has an important role on the performance of any iris recognition system. eyes nonlinear movement, occlusion, and specular reflection are main challenges for any iris segmentation method. in this pa...

Classic sparse representation for classification (SRC) method fails to incorporate the label information of training images, and meanwhile has a poor scalability due to the expensive computation for `1 norm. In this paper, we propose a novel subspace sparse coding method with utilizing label information to effectively classify the images in the subspace. Our new approach unifies the tasks of di...

We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes. The objective function maximizes the mutual information between what has been learned and what remains to be learned in terms of appearance information and cl...