Yuan Yan Tang

Face Recognition Under Varying Illumination Using Gradientfaces

In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the gradient faces. Theoretical analysis shows gradient faces is an illumination insensitive measure, and robust to different illumination, including uncontrolled, natural lighting. In addition, gradient faces is derived from the image gradient domain such that it can discover underlying inherent structure of face images since the gradient domain explicitly considers the relationships between neighboring pixel points. Therefore, gradient faces has more discriminating power than the illumination insensitive measure extracted from the pixel domain. Recognition rates of 99.83% achieved on PIE database of 68 subjects, 98.96% achieved on Yale B of ten subjects, and 95.61% achieved on Outdoor database of 132 subjects under uncontrolled natural lighting conditions show that gradient faces is an effective method for face recognition under varying illumination. Furthermore, the experimental results on Yale database validate that gradient faces is also insensitive to image noise and object artifacts (such as facial expressions).

High-Order Distance-Based Multiview Stochastic Learning in Image Classification

How do we find all images in a larger set of images which have a specific content? Or estimate the position of a specific object relative to the camera? Image classification methods, like support vector machine (supervised) and transductive support vector machine (semi-supervised), are invaluable tools for the applications of content-based image retrieval, pose estimation, and optical character recognition. However, these methods only can handle the images represented by single feature. In many cases, different features (or multiview data) can be obtained, and how to efficiently utilize them is a challenge. It is inappropriate for the traditionally concatenating schema to link features of different views into a long vector. The reason is each view has its specific statistical property and physical interpretation. In this paper, we propose a high-order distance-based multiview stochastic learning (HD-MSL) method for image classification. HD-MSL effectively combines varied features into a unified representation and integrates the labeling information based on a probabilistic framework. In comparison with the existing strategies, our approach adopts the high-order distance obtained from the hypergraph to replace pairwise distance in estimating the probability matrix of data distribution. In addition, the proposed approach can automatically learn a combination coefficient for each view, which plays an important role in utilizing the complementary information of multiview data. An alternative optimization is designed to solve the objective functions of HD-MSL and obtain different views on coefficients and classification scores simultaneously. Experiments on two real world datasets demonstrate the effectiveness of HD-MSL in image classification.

Multi-agent oriented constraint satisfaction

This paper presents a multi-agent oriented method for solving CSPs (Constraint Satisfaction Problems). In this method, distributed agents represent variables and a two-dimensional grid-like environment in which the agents inhabit corresponds to the domains of the variables. Thus, the positions of the agents in such an environment constitute the solution to a CSP. In order to reach a solution state, the agents will rely on predefined local reactive behaviors; namely, better-move, least-move, and random-move. While presenting the formalisms and algorithm, we will analyze the correctness and complexity of the algorithm, and demonstrate the proposed method with two benchmark CSPs, i.e., n-queen problems and coloring problems. In order to further determine the effectiveness of different reactive behaviors, we will examine the performance of this method in deriving solutions based on behavior prioritization and different selection probabilities.

Multiscale facial structure representation for face recognition under varying illumination

Facial structure of face image under lighting lies in multiscale space. In order to detect and eliminate illumination effect, a wavelet-based face recognition method is proposed in this paper. In this work, the effect of illuminations is effectively reduced by wavelet-based denoising techniques, and meanwhile the multiscale facial structure is generated. Among others, the proposed method has the following advantages: (1) it can be directly applied to single face image, without any prior information of 3D shape or light sources, nor many training samples; (2) due to the multiscale nature of wavelet transform, it has better edge-preserving ability in low frequency illumination fields; and (3) the parameter selection process is computationally feasible and fast. Experiments are carried out upon the Yale B and CMU PIE face databases, and the results demonstrate that the proposed method achieves satisfactory recognition rates under varying illumination conditions.

Off-line signature verification by the tracking of feature and stroke positions

There are inevitable variations in the signature patterns written by the same person. The variations can occur in the shape or in the relative positions of the characteristic features. In this paper, two methods are proposed to track the variations. Given the set of training signature samples, the first method measures the positional variations of the one-dimensional projection profiles of the signature patterns; and the second method determines the variations in relative stroke positions in the two-dimension signature patterns. The statistics on these variations are determined from the training set. Given a signature to be verified, the positional displacements are determined and the authenticity is decided based on the statistics of the training samples. For the purpose of comparison, two existing methods proposed by other researchers were implemented and tested on the same database. Furthermore, two volunteers were recruited to perform the same verification task. Results show that the proposed system compares favorably with other methods and outperforms the volunteers.

Topology Preserving Non-negative Matrix Factorization for Face Recognition

In this paper, a novel topology preserving non-negative matrix factorization (TPNMF) method is proposed for face recognition. We derive the TPNMF model from original NMF algorithm by preserving local topology structure. The TPNMF is based on minimizing the constraint gradient distance in the high-dimensional space. Compared with L2 distance, the gradient distance is able to reveal latent manifold structure of face patterns. By using TPNMF decomposition, the high-dimensional face space is transformed into a local topology preserving subspace for face recognition. In comparison with PCA, LDA, and original NMF, which search only the Euclidean structure of face space, the proposed TPNMF finds an embedding that preserves local topology information, such as edges and texture. Theoretical analysis and derivation given also validate the property of TPNMF. Experimental results on three different databases, containing more than 12 000 face images under varying in lighting, facial expression, and pose, show that the proposed TPNMF approach provides a better representation of face patterns and achieves higher recognition rates than NMF.

A Local Contrast Method for Small Infrared Target Detection

Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the local contrast map of the input image is obtained using the proposed local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. At the second stage, an adaptive threshold is adopted to segment the target. The experiments on two sequences have validated the detection capability of the proposed target detection method. Experimental evaluation results show that our method is simple and effective with respect to detection accuracy. In particular, the proposed method can improve the SNR of the image significantly.

Generalized Discriminant Analysis: A Matrix Exponential Approach

Linear discriminant analysis (LDA) is well known as a powerful tool for discriminant analysis. In the case of a small training data set, however, it cannot directly be applied to high-dimensional data. This case is the so-called small-sample-size or undersampled problem. In this paper, we propose an exponential discriminant analysis (EDA) technique to overcome the undersampled problem. The advantages of EDA are that, compared with principal component analysis (PCA) + LDA, the EDA method can extract the most discriminant information that was contained in the null space of a within-class scatter matrix, and compared with another LDA extension, i.e., null-space LDA (NLDA), the discriminant information that was contained in the non-null space of the within-class scatter matrix is not discarded. Furthermore, EDA is equivalent to transforming original data into a new space by distance diffusion mapping, and then, LDA is applied in such a new space. As a result of diffusion mapping, the margin between different classes is enlarged, which is helpful in improving classification accuracy. Comparisons of experimental results on different data sets are given with respect to existing LDA extensions, including PCA + LDA, LDA via generalized singular value decomposition, regularized LDA, NLDA, and LDA via QR decomposition, which demonstrate the effectiveness of the proposed EDA method.

Adaptive image segmentation with distributed behavior-based agents

Presents an autonomous agent-based image segmentation approach. In this approach, a digital image is viewed as a two-dimensional cellular environment which the agents inhabit and attempt to label homogeneous segments. In so doing, the agents rely on some reactive behaviors such as breeding and diffusion. The agents that are successful in finding the pixels of a specific homogeneous segment will breed offspring agents inside their neighboring regions. Hence, the offspring agents will become likely to find more homogeneous-segment pixels. In the mean time, the unsuccessful agents will be inactivated, without further search in the environment.

Automatic document processing: A survey

Abstract Surveys of the basic concepts and underlying techniques are presented in this paper. A basic model for document processing is described. In this model, document processing can be divided into two phases: document analysis and document understanding. A document has two structures: geometric (layout) structure and logical structure. Extraction of the geometric structure from a document refers to document analysis; mapping the geometric structure into logical structure deals with document understanding. Both types of document structures and the two areas of document processing are discussed. Two categories of methods have been used in document analysis, namely, (1) hierarchical methods including top-down and bottomdashup approaches, (2) no-hierarchical methods including modified fractal signature. Tree transform, formatting knowledge and description language approaches have been used in document understanding. A particular case of form document processing is discussed. Form description and form registration approaches are presented. A form processing system is also introduced. Finally, many techniques, such as skew detection, Hough transform, Gabor filters, projection, crossing counts, form definition language, etc. which have been used in these approaches are discussed.