The Experts below are selected from a list of 53811 Experts worldwide ranked by ideXlab platform
Samy Bengio - One of the best experts on this subject based on the ideXlab platform.
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database protocols and tools for evaluating score level Fusion algorithms in biometric authentication
Pattern Recognition, 2006Co-Authors: Samy BengioAbstract:Fusing the scores of several biometric systems is a very promising approach to improve the overall system's accuracy. Despite many works in the literature, it is surprising that there is no coordinated effort in making a benchmark database available. It should be noted that Fusion in this context consists not only of multimodal Fusion, but also intramodal Fusion, i.e., fusing systems using the same biometric modality but different features, or same features but using different classifiers. Building baseline systems from scratch often prevents researchers from putting more efforts in understanding the Fusion Problem. This paper describes a database of scores taken from experiments carried out on the XM2VTS face and speaker verification database. It then proposes several Fusion protocols and provides some state-of-the-art tools to evaluate the Fusion performance.
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a score level Fusion benchmark database for biometric authentication
Lecture Notes in Computer Science, 2005Co-Authors: Norman Poh, Samy BengioAbstract:Fusing the scores of several biometric systems is a very promising approach to improve the overall system's accuracy. Despite many works in the literature, it is surprising that there is no coordinated effort in making a benchmark database available. It should be noted that Fusion in this context consists not only of multimodal Fusion, but also intramodal Fusion, i.e., fusing systems using the same biometric modality but different features, or same features but using different classifiers. Building baseline systems from scratch often prevents researchers from putting more efforts in understanding the Fusion Problem. This paper describes a database of scores taken from experiments carried out on the XM2VTS face and speaker verification database. It then proposes several Fusion protocols and provides some state-of-the-art tools to evaluate the Fusion performance.
Jeff S. Shamma - One of the best experts on this subject based on the ideXlab platform.
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Consensus Filters for Sensor Networks and Distributed Sensor Fusion
Proceedings of the 44th IEEE Conference on Decision and Control, 2005Co-Authors: Reza Olfati-saber, Jeff S. ShammaAbstract:Consensus algorithms for networked dynamic systems provide scalable algorithms for sensor Fusion in sensor networks. This paper introduces a distributed filter that allows the nodes of a sensor network to track the average of n sensor measurements using an average consensus based distributed filter called consensus filter. This consensus filter plays a crucial role in solving a data Fusion Problem that allows implementation of a scheme for distributed Kalman filtering in sensor networks. The analysis of the convergence, noise propagation reduction, and ability to track fast signals are provided for consensus filters. As a byproduct, a novel critical phenomenon is found that relates the size of a sensor network to its tracking and sensor Fusion capabilities. We characterize this performance limitation as a tracking uncertainty principle. This answers a fundamental question regarding how large a sensor network must be for effective sensor Fusion. Moreover, regular networks emerge as efficient topologies for distributed Fusion of noisy information. Though, arbitrary overlay networks can be used. Simulation results are provided that demonstrate the effectiveness of consensus filters for distributed sensor Fusion.
Swagatam Das - One of the best experts on this subject based on the ideXlab platform.
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multi sensor data Fusion using support vector machine for motor fault detection
Information Sciences, 2012Co-Authors: Tribeni Prasad Banerjee, Swagatam DasAbstract:Motor fault diagnosis in dynamic condition is a typical multi-sensor data Fusion Problem. It involves the use of information collected from multiple sensors, such as vibration, sound, current, voltage, and temperature, to detect and identify motor faults. From the viewpoint of evidence theory, information obtained from each sensor can be considered as a piece of evidence, and as such, the multi-sensor based motor fault diagnosis can be viewed as the Problem of evidence Fusion. In this article we propose and investigate a hybrid method for fault signal classification based on sensor data Fusion by using the Support Vector Machine (SVM) and Short Term Fourier Transform (STFT) techniques. We report a practical application of this hybrid model and evaluate its performance. Finally, we compare the performance of the proposed system against some other standard fault classification techniques.
Fuyuan Xiao - One of the best experts on this subject based on the ideXlab platform.
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a new divergence measure for belief functions in d s evidence theory for multisensor data Fusion
Information Sciences, 2020Co-Authors: Fuyuan XiaoAbstract:Abstract Dempster–Shafer (D–S) evidence theory is useful for handling uncertainty Problems in multisensor data Fusion. However, the question of how to handle highly conflicting evidence in D–S evidence theory is still an open issue. In this paper, a new reinforced belief divergence measure, called RB , is developed to measure the discrepancy between basic belief assignments (BBAs) in D–S evidence theory. The proposed RB divergence is the first such measure to consider the correlations between both belief functions and subsets of the sets of belief functions, thus allowing it to provide a more convincing and effective solution for measuring the discrepancy between BBAs. Additionally, the RB divergence has certain benefits in terms of measurement. In particular, it has the properties of nonnegativeness, nondegeneracy, symmetry and satisfaction of the triangle inequality. Based on the RB divergence, an algorithm for multisensor data Fusion is then designed. Through a comparative analysis, it is verified that the proposed method is more feasible and reasonable than previous methods for measuring the divergence between BBAs. Finally, the proposed algorithm is effectively applied to a real-world classification Fusion Problem.
Zhenjiang Zhang - One of the best experts on this subject based on the ideXlab platform.
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belief function based decision Fusion for decentralized target classification in wireless sensor networks
Sensors, 2015Co-Authors: Wenyu Zhang, Zhenjiang ZhangAbstract:Decision Fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification Fusion Problem in wireless sensor networks (WSNs) and a new simple but effective decision Fusion rule based on belief function theory is proposed. Unlike existing belief function based decision Fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output conFusion matrix and real-time observations. We also derive explicit global BBA in the Fusion center under Dempster’s combinational rule, making the decision making operation in the Fusion center greatly simplified. Also, sending the whole BBA structure to the Fusion center is avoided. Experimental results demonstrate that the proposed Fusion rule has better performance in Fusion accuracy compared with the naive Bayes rule and weighted majority voting rule.
