Iris recognition has been demonstrated to be an efficient
technology for doing personal identification. Performance of iris
recognition system depends on the isolation of the iris region
from rest of the eye image. In this work, effective use of active
shape models (ASMs) for doing iris segmentation is demonstrated. A
method for building flexible model by learning patterns of iris
invariability from a well organized training set is described. The
specific approach taken in the work sacrifices generality, in
order to accommodate better iris segmentation. The algorithm was
initially applied on the on-angle, noise free CASIA data base and
then was extended to the off-axis iris images collected at WVU eye
center. A direct comparison with canny iris segmentation in terms
of error rates, demonstrate effectiveness of ASM segmentation. For
the selected threshold value of 0.4, FAR and FRR values were
0.13% and 0.09% using canny detectors and 0% each using
the proposed ASM based method.
Iris recognition has been demonstrated to be an efficient technology for doing personal identification. In this work, a method to perform iris recognition using biorthogonal wavelets is introduced. Effective use of biorthogonal wavelets using a lifting technique to encode the iris information is demonstrated. This new method minimizes built in noise of iris images using in-band thresholding in order to provide better mapping and encoding of the relevant information. Comparison of Gabor encoding, similar to the method used by Daugman and others, and biorthogonal wavelet encoding is performed. While Daugman's approach is a well-proven algorithm, the effectiveness of our algorithm is shown for the CASIA database, based on the ability to classify inter and intra class distributions, and may provide more flexibility for non-ideal images. The method was tested on the CASIA dataset of iris images with over 4,536 intra-class and 566,244 inter-class comparisons made. After calculating Hamming distances and for the selected threshold value of 0.4, FRR and FAR values were 13.6%
and 0.6% using Gabor filter and 0% and 0.03% using the biorthogonal wavelets.
In this work, a method to provide fingerprint vitality
authentication, in order to improve vulnerability of fingerprint
identification systems to spoofing is introduced. The method aims
at detecting 'liveness' in fingerprint scanners by using the
physiological phenomenon of perspiration. A wavelet based approach
is used which concentrates on the changing coefficients using the
zoom-in property of the wavelets. Multiresolution analysis and
wavelet packet analysis are used to extract information from low
frequency and high frequency content of the images respectively.
Daubechies wavelet is designed and implemented to perform the
wavelet analysis. A threshold is applied to the first difference
of the information in all the sub-bands. The energy content of the
changing coefficients is used as a quantified measure to perform
the desired classification, as they reflect a perspiration
pattern. A data set of approximately 30 live, 30 spoof, and 14
cadaver fingerprint images was divided with first half as a
training data while the other half as the testing data. The
proposed algorithm was applied to the training data set and was
able to completely classify 'live' fingers from 'not live'
fingers, thus providing a method for enhanced security and
improved spoof protection.