Abstract: During recent years, the traditional learning
approaches have undergone fundamental changes due to the
emergence of new technologies such as multimedia, hypermedia and
telecommunication. E-learning is a modern world phenomenon that
has come into existence in the information age and in a knowledgebased
society. E-learning has developed significantly within a short
period of time. Thus it is of a great significant to secure information,
allow a confident access and prevent unauthorized accesses. Making
use of individuals- physiologic or behavioral (biometric) properties is
a confident method to make the information secure. Among the
biometrics, fingerprint is more acceptable and most countries use it as
an efficient methods of identification. This article provides a new
method to compare the fingerprint comparison by pattern recognition
and image processing techniques. To verify fingerprint, the shortest
distance method is used together with perceptronic multilayer neural
network functioning based on minutiae. This method is highly
accurate in the extraction of minutiae and it accelerates comparisons
due to elimination of false minutiae and is more reliable compared
with methods that merely use directional images.
Abstract: Fuzzy fingerprint vault is a recently developed cryptographic construct based on the polynomial reconstruction problem to secure critical data with the fingerprint data. However, the previous researches are not applicable to the fingerprint having a few minutiae since they use a fixed degree of the polynomial without considering the number of fingerprint minutiae. To solve this problem, we use an adaptive degree of the polynomial considering the number of minutiae extracted from each user. Also, we apply multiple polynomials to avoid the possible degradation of the security of a simple solution(i.e., using a low-degree polynomial). Based on the experimental results, our method can make the possible attack difficult 2192 times more than using a low-degree polynomial as well as verify the users having a few minutiae.
Abstract: In this manuscript, a wavelet-based blind
watermarking scheme has been proposed as a means to provide
security to authenticity of a fingerprint. The information used for
identification or verification of a fingerprint mainly lies in its
minutiae. By robust watermarking of the minutiae in the fingerprint
image itself, the useful information can be extracted accurately even
if the fingerprint is severely degraded. The minutiae are converted in
a binary watermark and embedding these watermarks in the detail
regions increases the robustness of watermarking, at little to no
additional impact on image quality. It has been experimentally shown
that when the minutiae is embedded into wavelet detail coefficients
of a fingerprint image in spread spectrum fashion using a
pseudorandom sequence, the robustness is observed to have a
proportional response while perceptual invisibility has an inversely
proportional response to amplification factor “K". The DWT-based
technique has been found to be very robust against noises,
geometrical distortions filtering and JPEG compression attacks and is
also found to give remarkably better performance than DCT-based
technique in terms of correlation coefficient and number of erroneous
minutiae.
Abstract: This paper describes a complex energy signal model
that is isomorphic with digital human fingerprint images. By using
signal models, the problem of fingerprint matching is transformed
into the signal processing problem of finding a correlation between
two complex signals that differ by phase-rotation and time-scaling. A
technique for minutiae matching that is independent of image
translation, rotation and linear-scaling, and is resistant to missing
minutiae is proposed. The method was tested using random data
points. The results show that for matching prints the scaling and
rotation angles are closely estimated and a stronger match will have a
higher correlation.