Fingerprint recognition systems have become prevalent in various security applications. However, recent studies have
shown that it is not difficult to deceive the system with fake fingerprints made of silicon or gelatin. The fake fingerprints
have almost the same ridge-valley patterns as ones of genuine fingerprints so that conventional systems are unable to
detect fake fingerprints without a particular detection method. Many previous works against fake fingers required extra
sensors; thus, they lacked practicality. This paper proposes a practical and effective method that detects fake fingerprints,
using only an image sensor. Two criteria are introduced to differentiate genuine and fake fingerprints: the histogram
distance and Fourier spectrum distance. In the proposed method, after identifying an input fingerprint of a user, the
system computes two distances between the input and the reference that comes from the registered fingerprints of the
user. Depending on the two distances, the system classifies the input as a genuine fingerprint or a fake. In the experiment,
2,400 fingerprint images including 1,600 fakes were tested, and the proposed method has shown a high recognition rate
of 95%. The fake fingerprints were all accepted by a commercial system; thus, the use of these fake fingerprints qualifies
the experiment.
The fingerprint recognition is the most developed field in the biometrics recognition and it can apply to the various
applications. So the various studies are in progress for an enhancement of fingerprint recognition performance. In this
paper, we will study the fingerprint recognition sensor. The fingerprint becomes input using fingerprint sensor to the
fingerprint recognition system and the performance of the sensor has an effect on the fingerprint quality. Therefore, the
improvement of system performance is possible by improving the performance of sensor.
In this paper, we will study sensors which can be protected or unprotected from artificial fingerprint attack. We make
various artificial fingerprints, and test sensors to overcome artificial fingerprint attack. We will analyze results of
scanning test according to the various sensors and propose the method using the histogram of the normal image in order
to measure the performance of these sensors. The measured characteristics are the resolution, shift, gradient, contrast,
and rotation. The purpose of this paper is to propose the ways of the performance measurement which can be a criterion
to evaluate the sensor performance.
We study cross-talk noise in volume holographic memory with fractional Fourier transform. For the volume holographic medium with finite dynamic range for the linearity fractional Fourier transform has an advantage over the conventional Fourier transform because it yields a spectral distribution with no high peak.
Fractional correlation is an extension of the conventional correlation. It employs fractional Fourier transform (FRFT) that includes the conventional Fourier transform as a special case where the order of the FRFT equals one. Because of the FRFT's lack of the shift-invariant property, the FRFT is not applicable to the conventional joint transform correlator, but to the nonconventional joint transform correlator (NJTC) that have been proposed by F. T. S. Yu et al., in which separate lenses transform the input signals and their spectral distributions overlap on the square-law detector. This provides an optical implementation of the fractional correlation. The conventional Fourier transform generally yields a high peak at the center of the spectral plane. But the FRFT gives a spectral distribution with no high peak, which is desirable because the square-law detector has a finite dynamic range for the linearity. Moreover, we prove that the fractional correlation produces a narrower output distribution and has the same correlation value at the center of the output plane as the conventional correlation. The conventional correlation has the shift-invariant property, but the fractional correlation has not.
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