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An Efficient Hand-Based Biometric Recognition System Using Finger- Knuckle-Print Data

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Automatic personal identification is playing an important role in secure and reliable applications, such as access control, surveillance systems, information systems, physical buildings and many more applications. In contrast with traditional approaches, based on what a person knows (password) or what a person has (tokens), biometric based identification is providing an improved security for their users. Biometrics is the measurement of physiological traits such as palmprints, fingerprints, iris etc., and/or behavioral traits such as gait, signature etc., of an individual person for personal recognition. Hand-based person identification provides a good user acceptance, distinctiveness, universality, relatively easy to capture and low-cost. However, Finger-Knuckle-Print (FKP), which provides different information from a variety of finger types, has been recently used to improve the performance of hand-based biometric identification because each finger has a specific feature, making it possible to collect more information to improve the accuracy of hand-based biometric systems. In this paper, we presented an efficient online personal identification based on FKP using the twodimensional Block based Discrete Cosine Transform (2D-BDCT) and MultiVariate Normal density function (MVN). In this study, a segmented FKP is firstly divided into non-overlapping and equal-sized blocks, and then, applies the 2DBDCT over each block. By using zig-zag scan order, each transform block is reordered to produce the feature vector. Subsequently, we use the MVN for modeling the feature vector of each FKP. Finally, Log-likelihood scores are used for FKP matching. Finally, performance of all finger types is determined individually and several fusion rules are applied to develop a multimodal system based on fusion at the matching score level. Experimental results show that FKPs modalities show best performance for identifying a person as they provide an excellent identification rate and with more security. Here we also discuss few patents that are relevant to the article.
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Keywords: 2D Block based Discrete Cosine Transform (2D-BDCT); Biometrics; Data fusion; Finger-Knuckle-Print (FKP); Identification; Log-likelihood; Multivariate Normal density (MVN); Sum of Absolute Differences (SAD)

Document Type: Research Article

Publication date: December 1, 2012

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  • Recent Patents on Telecommunications publishes review and research articles, and guest edited thematic issues by experts on recent patents in telecommunications. The journal also covers recent research (where patents have been registered) in fast emerging areas of the field such as networks, wireless communications, communication technology, computing and processing, and the Internet. A selection of important and recent patents on telecommunications is also included in the journal. The journal is essential reading for all researchers involved in telecommunication science and technology.
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