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- W1965441492 abstract "Automatic personal identification from their physical and behavioral traits, called biometrics technologies, is now needed in many fields such as: surveillance systems, security systems, physical buildings and many more applications. In this paper, we propose an efficient online personal identification system based on Finger-Knuckle-Print (FKP) using the Hidden Markov Model (HMM) and two-dimensional Block based Discrete Cosine Transform (2D-BDCT). In this study, a segmented FKP is firstly divided into non- overlapping and equal-sized blocks, and then, applies the 2D-BDCT over each block. By using zigzag scan order (starting at the top-left) each transform block is reordered to produce the feature vector. Subsequently, we use the HMM for modeling the feature vector of each FKP. Finally, Log-likelihood scores are used for FKP matching. Our experimental results show the effectiveness and reliability of the proposed approach, which brings both high identification and accuracy rate." @default.
- W1965441492 created "2016-06-24" @default.
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- W1965441492 date "2015-05-12" @default.
- W1965441492 modified "2023-09-22" @default.
- W1965441492 title "Finger-Knuckle-Print identification System Using Hidden Markov Model and Discret Cosine Transform" @default.
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- W1965441492 doi "https://doi.org/10.15849/icit.2015.0049" @default.
- W1965441492 hasPublicationYear "2015" @default.
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