Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.

A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm

A lightweight and robust two-factor authentication scheme for personalized healthcare systems using wireless medical sensor networks

EPCBIR: An efficient and privacy-preserving content-based image retrieval scheme in cloud computing

The original Pawlaks rough set approach based on indiscernibility relation (single granularity) has been extended to multigranulation rough set structure in the recent years. Multigranulation rough set approach has become a flouring research direction in rough set theory. This paper considers rough approximation of a fuzzy concept under the framework of multigranulation over two different universes of discourse, i.e., multigranulation fuzzy rough set models over two universes. We present three types of multigranulation fuzzy rough set over two universes by the constructive approach, respectively. Some interesting properties of the proposed models are discussed and also the interrelationships between the proposed models and the existing rough set models are given. We then propose a new approach to a kind of multiple criteria group decision making problem based on multigranulation fuzzy rough set model over two universes. The decision rules and algorithm of the proposed method are given and an example of handling multiple criteria group decision making problem of clothes ranking illustrates this approach. The main contribution of this paper is twofold. One is to establish the multigranulation fuzzy rough set theory over two universes. Another is to try presenting a new approach to multiple criteria group decision making based on multigranulation fuzzy rough set over two universes. The proposed models not only enrich the theory of multigranulation rough set but also make a tentative to provide a new perspective for multiple criteria group decision making with uncertainty.

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Multigranulation fuzzy rough set over two universes and its application to decision making

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper.

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Security and Accuracy of Fingerprint-Based Biometrics: A Review

Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints. However, this system has some security and privacy problems. For example, some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification. Therefore, a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information. In this paper, a new software-based liveness detection approach using multi-scale local phase quantity (LPQ) and principal component analysis (PCA) is proposed. The feature vectors of a fingerprint are constructed through multi-scale LPQ. PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features. Finally, a training model is gained using support vector machine classifier, and the liveness of a fingerprint is detected on the basis of the training model. Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy. This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.

Fingerprint liveness detection based on multi-scale LPQ and PCA

In recent years, fingerprint authentication systems have been extensively deployed in various applications, including attendance systems, authentications on smartphones, mobile payment authorizations, as well as various safety certifications. However, similar to the other biometric identification technologies, fingerprint recognition is vulnerable to artificial replicas made from cheap materials, such as silicon, gelatin, etc. Thus, it is especially necessary to distinguish whether a given fingerprint is a live or a spoof one prior to such authentication. In order to solve the problems above, a novel local descriptor named Weber local binary descriptor for fingerprint liveness detection (FLD) has been proposed in this paper. The method consists of two components: the local binary differential excitation component that extracts intensity-variance features and the local binary gradient orientation component that extracts orientation features. The co-occurrence probability of the two components is calculated to construct a discriminative feature vector, which is fed into support vector machine (SVM) classifiers. The effectiveness of the proposed method is intuitively analyzed on the image samples and numerically demonstrated by Mahalanobis distance. Experiments are performed on two public databases from FLD competitions from 2011 and 2013. The results have proved that the proposed method obtains the best detection accuracy among the existing image local descriptors in FLD.

A Novel Weber Local Binary Descriptor for Fingerprint Liveness Detection

Fingerprint-based recognition systems have been increasingly deployed in various applications nowadays. However, the recognition systems can be spoofed by using an accurate imitation of a live fingerprint such as an artificially made fingerprint. In this paper, we propose a novel software-based fingerprint liveness detection method which achieves good detection accuracy. We regard the fingerprint liveness detection as a two-class classification problem and construct co-occurrence array from image gradients to extract features. In doing so, the quantization operation is firstly conducted on the images. Then, the horizontal and vertical gradients at each pixel are calculated, and the gradients of large absolute values are truncated into a reduced range. Finally, the second-order and the third-order co-occurrence arrays are constructed from the truncated gradients, and the elements of the co-occurrence arrays are directly used as features. The second-order and the third-order co-occurrence array features are separately utilized to train support vector machine classifiers on two publicly available databases used in Fingerprint Liveness Detection Competition 2009 and 2011. The experimental results have demonstrated that the features extracted with the third-order co-occurrence array achieve better detection accuracy than that with the second-order co-occurrence array and outperform the state-of-the-art methods.

Fingerprint liveness detection using gradient-based texture features

Fingerprint recognition systems are extensively deployed for the authentication in many applications. However, this kind of recognition systems may be spoofed by artificial fingerprints made from various materials. Thus, it is necessary to add a fingerprint liveness detection module to keep this kind of recognition systems on a good level of security. The fingerprint liveness detection (FLD) aims to judge whether a given fingerprint image is captured from a real finger or a spoof one. It is a typical two-class classification problem where the feature extraction is the key step. In this paper, we propose an effective feature extraction method for the FLD problem. The proposed features consist of two components, Weber local binary pattern (WLBP) and circularly symmetric Gabor feature (CSGF), analyzing the fingerprint images in both the spatial and frequency domains. The co-occurrence probabilities of the two components are calculated as the final features. The proposed features are utilized to train SVM classifiers separately on two databases in Fingerprint Liveness Detection Competition 2011 and 2013. Experimental results demonstrate the effectiveness of the proposed method.

Rotation-invariant Weber pattern and Gabor feature for fingerprint liveness detection

Fingerprint identification systems have been widely applied in both civilian and governmental applications due to its satisfying performance. However, the fingerprint identification systems can be easily cheated by the presentation of artificial fingerprints made from common materials. Therefore, it reduces the reliability and misleads the decision of the fingerprint identification systems. In this work, we propose a software-based fingerprint liveness detection method based on multiscale difference co-occurrence matrix (DCM). In doing so, multiscale wavelet transform operation is first conducted on the original image. After the preprocessing of the decomposition of the original image, DCMs are computed by using the Laplacian operator. Horizontal and vertical difference co-occurrence matrices are constructed in our method. In order to reduce the dimensionality of the feature vectors, truncation operation is introduced for DCMs. Then, the elements of processing DCMs are regarded as the texture features of original fingerprint images. Finally, classification accuracy of feature vectors is predicted based on a support vector machine classifier. The experimental results have shown that the performance of our method is very promising and meanwhile achieve better accurate classification compared with the best algorithms of LivDet2013 and LivDet2011, while being able to recognize spoofed fingerprints with better recognition accuracy.

Rui Lv

Papers

Fingerprint liveness detection based on multi-scale LPQ and PCA

A Novel Weber Local Binary Descriptor for Fingerprint Liveness Detection

Fingerprint liveness detection using gradient-based texture features

Rotation-invariant Weber pattern and Gabor feature for fingerprint liveness detection

Fingerprint liveness detection using multiscale difference co-occurrence matrix