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Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions...
The primary objective of this paper is to explore the applicability of sparse representation based classification (SRC), particularly at the fingerprint recognition problem. This paper proposes sparse proximity based fingerprint matching methodology. The sparse representation based classification problem can be solved as representing the test sample in terms of training set with some sparse residual...
In this paper, we propose a supervised dictionary learning algorithm that aims to preserve the local geometry in both dimensions of the data. A graph-based regularization explicitly takes into account the local manifold structure of the observations. A second graph regularization gives similar treatment to the feature domain and helps in learning a more robust dictionary. Both graphs can be constructed...
In this paper, three new algorithms are presented by applying group idea and collaborative thought to projective dictionary pair learning (DPL). These algorithms further extend the framework of discriminative dictionary learning (DL). Based on projective dictionary pair learning which realizes the goals of signal representation and pattern classification by learning a synthesis dictionary and an analysis...
Previous dictionary learning algorithms usually take the locality information of training samples into account in the learning process, and it may degrade the robustness of the dictionary. In this paper, an new locality constrained dictionary learning algorithm (LCDL) for face recognition by using the locality characters of atoms is proposed. Since the atoms are learned from the training samples,...
In this paper, we present a novel scheme for text-independent online writer identification. As a first contribution, we propose histogram based features, inspired from the area of object detection, to describe the structural primitives of handwriting. Secondly, we have used sparse coding techniques to learn prototypes, that describe the general writing characteristics of the authors. To the best of...
In this paper, we propose a new texture descriptor, completed local derivative pattern (CLDP). In contrast to completed local binary pattern (CLBP), which involves only local differences at each scale, CLDP encodes the directional variation of the local differences of two scales as a complementary component to local patterns in CLBP. The new component in CLDP, with regarded as the directional derivative...
People drive on the road and eat in the kitchen. Can the road imply driving or the kitchen imply eating? This paper addresses such a problem by studying the relations between actions and scenes. To get effective scene representation, we use a deep convolutional neural networks (CNN) model trained from a scene-centric database to predict scene responses for videos. We employ two encoding schemes based...
Accurate classification and recognition of pulmonary nodules is an important and key process of Computer-Aided Diagnosis (CAD) system in lung cancer diagnose. Although it has become an increasingly popular research topic, it remains a lot of scientific and technical challenges. Not only do we lack the accurate and effective algorithm of recognition and classification, but also we have difficulties...
In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The proposed sparse coding method involves a series of sub-dictionaries. Each sub-dictionary contains all the training samples except for those from one particular category. For the test...
The spatial pyramid feature learning methods, such as Spatial Pyramid Matching (SPM) and Sparse Coding based Spatial Pyramid Matching (ScSPM), have achieved significant performance in image categorization. While most of these methods are still based on manual-design features, such as SIFT, HOG and LBP, which limits the representation of data. In this paper, we propose a novel Sparse Autoencoder based...
Recently sparse and collaborative representation based classification has been developed for face recognition with single sample per person (SSPP). By using variations extracted from a generic training set as an additional common dictionary, promising performance has been reported in face recognition with SSPP. However, existing representation based classifiers for face recognition with SSPP ignored...
Behavior recognition from large available motion capture data has received wide attention in the computer animation community and is growing increasingly important in recent years. In this paper, we present an efficient motion capture behavior recognition approach via neighborhood preserving dictionary learning. First, we normalize all the motion sequences in the database to make the motion to be...
Depression and other mood disorders are common, disabling disorders with a profound impact on individuals and families. Inspite of its high prevalence, it is easily missed during the early stages. Automatic depression analysis has become a very active field of research in the affective computing community in the past few years. This paper presents a framework for depression analysis based on unimodal...
Tactile sensors in the robotic fingertips are used to capture multiple object properties such as texture, roughness, spatial features, compliance or friction and therefore becomes a very important sense modality for intelligent robot. However, existing work neglects the intrinsic relation between different fingers which simultaneously contact the object. In this paper, a joint kernel sparse coding...
We present a locality preserving K-SVD (LP-KSVD) algorithm for joint dictionary and classifier learning, and further incorporate kernel into our framework. In LP-KSVD, we construct a locality preserving term based on the relations between input samples and dictionary atoms, and introduce the locality via nearest neighborhood to enforce the locality of representation. Motivated by the fact that locality-related...
This paper analyses the unlinkability and the irreversibility of the iris biometric template protection system based on Bloom filters introduced at ICB 2013. Hermans et al. presented at BIOSIG 2014 an attack on the unlinkability of these templates. In the worst case, their attack succeeds with probability at least 96%. But in their attack, they assume protected templates generated from the same iriscode...
In this paper, we propose a new graph-based sparse coding and embedding (GSCE) method for activity-based human identification. Different from human activity recognition which recognizes different types of human activities such as walking, running, eating, and drinking, in this study, we aim to identify persons from his/her activities. To our best knowledge, this problem has been seldom investigated...
Matrix factorization techniques have been frequently utilized in pattern recognition and machine learning. Among them, Non-negative Matrix Factorization (NMF) has received considerable attention because it represents the naturally occurring data by parts of it. On the other hand, from the geometric perspective, the data is usually sampled from a low dimensional manifold embedded in a high dimensional...
We consider learning a discriminative dictionary in sparse representation and specifically focus on face recognition application to improve its performance. This paper presents an algorithm to learn a discriminative dictionary with low-rank regularization on the dictionary. To make the dictionary more discerning, we apply Fisher discriminant function to the coding coefficients with the goal that they...
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