Digital Image Processing
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
The Anti-spoofing Study of Vein Identification System
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
An evaluation of direct attacks using fake fingers generated from ISO templates
Pattern Recognition Letters
Fake finger detection by skin distortion analysis
IEEE Transactions on Information Forensics and Security
Time-series detection of perspiration as a liveness test in fingerprint devices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Expert Systems with Applications: An International Journal
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Recently, finger-vein recognition has received considerable attention. It is widely used in many applications because of its numerous advantages, such as the small capture device, high accuracy, and user convenience. Nevertheless, finger-vein recognition faces a number of challenges. One critical issue is the use of fake finger-vein images to carry out system attacks. To overcome this problem, we propose a new fake finger-vein image-detection method based on the analysis of finger-vein images in both the frequency and spatial domains. This research is novel in five key ways. First, very little research has been conducted to date on fake finger-vein image detection. We construct a variety of fake finger-vein images, printed on A4 paper, matte paper, and overhead projector film, with which we evaluate the performance of our system. Second, because our proposed method is based on a single captured image, rather than a series of successive images, the processing time is short, no additional image alignment is required, and it is very convenient for users. Third, our proposed method is software-based, and can thus be easily implemented in various finger-vein recognition systems without special hardware. Fourth, Fourier transform features in the frequency domain are used for the detection of fake finger-vein images; further, both spatial and frequency characteristics from Haar and Daubechies wavelet transforms are used for fake finger-vein image detection. Fifth, the detection accuracy of fake finger-vein images is enhanced by combining the features of the Fourier transform and Haar and Daubechies wavelet transforms based on support vector machines. Experimental results indicate that the equal error rate of fake finger-vein image detection with our proposed method is lower than that with a Fourier transform, wavelet transform, or other fusion methods.