BIROn - Birkbeck Institutional Research Online

    Finger texture verification systems based on multiple spectrum lighting sensors with four fusion levels

    Al-Kaltakchi, M. and Omar, R. and Abdullah, H. and Han, Tingting and Chambers, J. (2019) Finger texture verification systems based on multiple spectrum lighting sensors with four fusion levels. Iraqi Journal of Information & Communications Technology 1 (3), pp. 1-16. ISSN 2222-758X.

    [img] Text
    26629a.pdf - Author's Accepted Manuscript
    Restricted to Repository staff only until 1 February 2020.

    Download (1MB) | Request a copy

    Abstract

    Finger Texture (FT) is one of the most recent attractive biometric characteristic. It refers to a finger skin area which is restricted between the fingerprint and the palm print (just after including the lower knuckle). Different specifications for the FT can be obtained by employing multiple images spectrum of lights. Individual verification systems are established in this paper by using multiple spectrum FT specifications. The key idea here is that by combining two various spectrum lightings of FTs, high personal recognitions can be attained. Four types of fusion will be listed and explained here: Sensor Level Fusion (SLF), Feature Level Fusion (FLF), Score Level Fusion (ScLF) and Decision Level Fusion (DLF). Each fusion method is employed, examined for different rules and analysed. Then, the best performance procedure is benchmarked to be considered. From the database of Multiple Spectrum CASIA (MSCASIA), FT images have been collected. Two types of spectrum lights have been exploited (the wavelength of 460 nm, which represents a Blue (BLU) light, and the White (WHT) light). Supporting comparisons were performed, including the state-of-the-art. Best recognition performance was recorded for the FLF based concatenation rule by improving the Equal Error Rate (EER) percentages from 5% for the BLU and 7% for the WHT to 2%.

    Metadata

    Item Type: Article
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Tingting Han
    Date Deposited: 29 May 2019 05:47
    Last Modified: 30 Jul 2019 08:15
    URI: http://eprints.bbk.ac.uk/id/eprint/26629

    Statistics

    Downloads
    Activity Overview
    3Downloads
    30Hits

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item