Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27758
Title: Contactless finger knuckle identification using smartphones
Authors: Cheng, KY
Kumar, A 
Keywords: C++ language
Authorisation
Biometrics (access control)
Data privacy
Graphical user interfaces
Image segmentation
Object detection
Operating systems (computers)
Smart phones
Issue Date: 2012
Publisher: IEEE
Source: 2012 BIOSIG-Proceedings of the International Conference of the Biometrics Special Interest Group (BIOSIG), 6-7 September 2012, Darmstadt, p. 1-6 How to cite?
Abstract: This paper details the development of a smartphone based online system to automatically identify a person by using their finger knuckle image. The key objective is to exploit user-friendly biometric, with least privacy concern, to enhance security of the data in smartphone. The final product from this research is a finger knuckle authentication smartphone application, which is developed under Android operating system with environment version 2.3.3. This paper has developed some specialized algorithms for the finger knuckle detection, image pre-processing and region segmentation. Automatically detected and segmented finger knuckle images are used to encode finger knuckle pattern phase information using a pair of log-Gabor filters. Efficient implementation of various modules is achieved in C/C++ programming language, with OpenCV library, for online application. We also developed a user-friendly graphical user interface for the users to enroll and authenticate themselves. The developed system can therefore acquire finger knuckle image from the smartphone camera and automatically authenticate the genuine users. This paper has also developed a new smartphone based finger knuckle image database of 561 finger knuckle images of 187 different fingers from 109 users, in real imaging environment. In the best of our knowledge, this is the first attempt to develop a mobile phone based finger knuckle identification which has shown highly promising results in automatically identifying the users from their finger knuckle images.
URI: http://hdl.handle.net/10397/27758
ISBN: 978-1-4673-1010-9
ISSN: 1617-5468
Appears in Collections:Conference Paper

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