Ying Chen

Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

Abstract. A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face-iris-fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single-scalar score via a trained, supported, vector regression model for a final decision. A large-scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for face-iris-fingerprint multimodal biometric systems.

Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-regions weights and then weighted different subregions matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

Wireless Sensor Networks Security Issues as Smart Materials Systems

With the wide application of wireless sensor networks, their securities become research hotspot. Firstly, introduced the limiting factors of wireless sensor networks, summarized the security attack types and gave prevention strategies accordingly. Secondly, proposed the security technologies of wireless sensor networks, these technologies include key management and distribution, authentication and verification, and data fusion encryption technologies. Finally, discussed further security research interests.

Time Management Model of Workflow Based on Time Axis

The goal of workflow system is to guarantee that the right activities are executed and completed within the correct and ideal time periods via automation, moreover, in order to help a company to be sufficiently competitive, the workflow system it adopts should be able to ensure activities are carried out to the maximum extent as possible and to manage several workflow instances at the same time. Hence, sufficient consideration of temporal constraints and efficiency should be taken into the design process of workflow system. This paper proposes a model of workflow control based on time axis which is able to ensure one or more workflows to advance in correct time period efficiently and precisely.

Analysis of Image Texture Features Based on Gray Level Co-Occurrence Matrix

Gray level co-occurrence matrix (GLCM) is a second-order statistical measure of image grayscale which reflects the comprehensive information of image grayscale in the direction, local neighborhood and magnitude of changes. Firstly, we analyze and reveal the generation process of gray level co-occurrence matrix from horizontal, vertical and principal and secondary diagonal directions. Secondly, we use Brodatz texture images as samples, and analyze the relationship between non-zero elements of gray level co-occurrence matrix in changes of both direction and distances of each pixels pair by. Finally, we explain its function of the analysis process of texture. This paper can provided certain referential significance in the application of using gray level co-occurrence matrix at quality evaluation of texture image.

Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

Robust iris segmentation algorithm based on self-adaptive Chan–Vese level set model

Abstract. Iris segmentation is the first and critical step in an iris recognition system. A robust iris segmentation algorithm based on the self-adaptive Chan and Vese (SACV) level set model is proposed. First, the process of constructing the SACV model based on analyses of the corresponding requirements for the CV model is described when it is applied on iris segmentation. Second, the coarse segmentation of pupil and iris, which are localized based on image pixel gray information, is used as the initial contour of the SACV model. Third, the interference factors, such as eyelashes and eyelids, are detected and evaluated simultaneously to generate the interference degree and then the related parameter of SACV is set according to the interference degree. Finally, SACV is used to conduct the final fine segmentation of the pupil and iris. Experiments on four public iris image databases (e.g., CASIA-V1, CASIA-V3 Interval, CASIA-V3 Lamp, and MMU-V1) demonstrate the segmentation accuracy performance of the proposed algorithm, and at the same time, the proposed algorithm also displays robust performance in noisy situations, such as Gaussian, Poisson, salt-and-pepper, and speckle noises. Moreover, comparisons with the well-known methods further show that our algorithm can segment iris images more accurately.

Design and Implement of a Flexible Workflow Model Based on UML Modeling Technology

As the rapid development of modern society, its hard for traditional workflow technologies to meet the variant requirements inside an enterprise. The research focus of workflow technology has been moved to flexibly adjustable workflow models that can easily and fast adapt to business change. In terms of this point, a flexible workflow model is put forward based on the UML technology. Several UML diagrams, such as class diagram, activity diagram and sequence diagram, are introduced to illustrate both model elements and detailed working mechanism of the workflow model. In addition, the strategy of flexible adaption is proposed to deal with changes. Based on this model, a general-purpose system of project application and review is designed and implemented, which is able to adapt flexibly to the business flow changes of project applying after putting into use.

A Workflow Management Model Based on Workflow Node Property

Workflow management systems are the powerful tools as well as the best supports for industries which involve series of complex workflows. Specifically, two of the main objectives of workflows management system are (1) ensuring the correctness and integration of workflow advancement, and (2) carrying workflow forward to the maximum extent automatically. To ensure the correctness and integration of workflow management system, in this paper, a workflow management method based on the workflow node property is proposed, and a workflow management system model is given. In addition, in the given model, an automatic advance mode is proposed to make the workflow is able to be carried on automatically.

Research on Characteristic Properties of Gray Level Co-Occurrence Matrix

Gray level co-occurrence matrix (GLCM) is a second-order statistical measurement. In order to understand the characterization degree of GLCM’s different feature properties, we use images of Brodatz texture images as experimental samples, analyze the change process of feature properties in horizontal, vertical and principal and secondary diagonal directions under the situation of some elements’ dynamic changes such as distance of pixels pair, size of moving window and gray level quantization,. By analyzing the experimental results, this paper can provided certain referential significance in how to select feature properties reasonable in the application of image retrieval and classification and identification which are based on using GLCM as feature.