Intra-Class Threshold Generation in Multimodal Biometric Systems by Set Estimation Technique
01 Jul 2013-International Journal of Software Science and Computational Intelligence (IGI Global)-Vol. 5, Iss: 3, pp 22-32
TL;DR: An intra-class threshold for multimodal biometric recognition procedure has been developed and is found to perform better than traditional ROC curve based threshold technique.
Abstract: Biometric recognition techniques attracted the researchers for the last two decades due to their many applications in the field of security. In recent times multimodal biometrics have been found to perform better, in several aspects, over unimodal biometrics. The classical approach for recognition is based on dissimilarity measure and for the sake of proper classification one needs to put a threshold on the dissimilarity value. In this paper an intra-class threshold for multimodal biometric recognition procedure has been developed. The authors' selection method of threshold is based on statistical set estimation technique which is applied on a minimal spanning tree and consisting of fused face and iris images. The fusion is performed here on feature level using face and iris biometrics. The proposed method, applied on several multimodal datasets, found to perform better than traditional ROC curve based threshold technique.
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Journal Article•
TL;DR: This paper combines face and iris features for developing a multimodal biometrics approach, which is able to diminish the drawback of single biometric approach as well as to improve the performance of authentication system.
Abstract: The recognition accuracy of a single biometric authentication system is often much reduced due to the environment, user mode and physiological defects. In this paper, we combine face and iris features for developing a multimode biometric approach, which is able to diminish the drawback of single biometric approach as well as to improve the performance of authentication system. We combine a face database ORL and iris database CASIA to construct a multimodal biometric experimental database with which we validate the proposed approach and evaluate the multimodal biometrics performance. The experimental results reveal the multimodal biometrics verification is much more reliable and precise than single biometric approach.
53 citations
TL;DR: This paper provides an overview over the relationship between Petri Nets and Discrete Event Systems as they have been proved as key factors in the cognitive processes of perception and memorization.
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TL;DR: In this chapter, an effective temporal-based event detection framework is proposed to support high-level video indexing and retrieval to tackle the challenges caused by loose video structure and class imbalance issues.
Abstract: The fast proliferation of video data archives has increased the need for automatic video content analysis and semantic video retrieval. Since temporal information is critical in conveying video content, in this chapter, an effective temporal-based event detection framework is proposed to support high-level video indexing and retrieval. The core is a temporal association mining process that systematically captures characteristic temporal patterns to help identify and define interesting events. This framework effectively tackles the challenges caused by loose video structure and class imbalance issues. One of the unique characteristics of this framework is that it offers strong generality and extensibility with the capability of exploring representative event patterns with little human interference. The temporal information and event detection results can then be input into our proposed distributed video retrieval system to support the high-level semantic querying, selective video browsing and event-based video retrieval.
3 citations
01 Jan 2012
TL;DR: The question is how to extract experience from a set of examples for the use of expert systems, as machine-learning algorithms such as “learning from example” claim that they are able to extract knowledge from experience.
Abstract: Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems. Machine-learning algorithms such as “learning from example” claim that they are able to extract knowledge from experience. Symbolic systems as, for example, ID3 (Quinlan, 1983) and versionspace (Mitchell, 1982) are capable of learning from examples. Connectionist systems claim to have advantages over these systems in generalization and in handling noisy and incomplete data. For every data set, the rule-based systems have to find a definite diagnosis. Inconsistent data can force symbolic systems into an indefinite state. In connectionist networks, a distributed representation R. Manjunath Bangalore University, India Differential Learning Expert System in Data Management
2 citations
References
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TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
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TL;DR: The DET Curve is introduced as a means of representing performance on detection tasks that involve a tradeoff of error types and why it is likely to produce approximately linear curves.
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1,516 citations
06 Sep 2004
TL;DR: The various scenarios that are possible in multimodal biometric systems, the levels of fusion that are plausible and the integration strategies that can be adopted to consolidate information are discussed.
Abstract: Unimodal biometric systems have to contend with a variety of problems such as noisy data, intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates. Some of these limitations can be addressed by deploying multimodal biometric systems that integrate the evidence presented by multiple sources of information. This paper discusses the various scenarios that are possible in multimodal biometric systems, the levels of fusion that are plausible and the integration strategies that can be adopted to consolidate information. We also present several examples of multimodal systems that have been described in the literature.
695 citations
01 Jan 1996
TL;DR: A prototype system for personnel verification based on automated iris recognition for noninvasive biometric measurement is described, in which the system exhibits flawless performance in the evaluation of 520 iris images.
Abstract: This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endevour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine-vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study in which the system exhibits flawless performance in the evaluation of 520 iris images.
422 citations
Additional excerpts
...Secured MMB systems (MMBS) should be capable of authenticating a person’s identity based on his biometric traits like faces (Bourlai, Kittler, & Messer, 2009; Geng & Jiang, 2013), fingerprints (Mehtre, 1993; Hiew, Teoh, & Yin, 2010), iris (Wildes et al., 1996)etc....
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01 Aug 2002
TL;DR: The purpose of this document is to summarise the current understanding of the biometrics community of the best scientific practices for conducting technical performance testing towards the end of field performance estimation.
Abstract: The purpose of this document is to summarise the current understanding of the biometrics community of the best scientific practices for conducting technical performance testing towards the end of field performance estimation.
402 citations