Protecting data is a critical part of life in the modern world. The science of protecting data, known as cryptography,
makes use of secret keys to encrypt data in a format that is not easily decipherable. However, most modern cryptography
systems use passwords to perform user authentication. These passwords are a weak link in the security chain, as well as
a common point of attack on cryptography schemes. One alternative to password usage is biometrics: using a person’s
physical characteristics to verify who the person is and unlock the data correspondingly. This study provides a concrete
implementation of the Cambridge biometric cryptosystem. In addition, hardware acceleration has been performed on the
system in order to reduce system runtime and energy usage, which is compared with software-level code optimization.
The experiment takes place on a Xilinx Zynq-7000 All Programmable SoC. Software implementation is run on one of
the embedded ARM A9 cores while hardware implementation makes use of the programmable logic. This has resulted in
an algorithm with strong performance characteristics in both energy usage and runtime.
Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences
on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time
between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris
images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson
University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on
iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of
iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris
recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek
algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris
recognition regression indicating the variability in match scores. According to the regression analysis, our study in this
paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility
to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.
Iris recognition has expanded from controlled settings to uncontrolled settings (on the move, from a distance)
where blur is more likely to be present in the images. More research is needed to quantify the impact of blur on iris
recognition. In this paper we study the effect of out-of-focus blur on iris recognition performance from images
captured with out-of-focus blur produced at acquisition. A key aspect to this study is that we are able to create a
range of blur based on changing focus of the camera during acquisition. We quantify the produced out-of-focus
blur based on the Laplacian of Gaussian operator and compare it to the gold standard of the modulation transfer
function (MTF) of a calibrated black/white chart. The sharpness measure uses an unsegmented iris images from a
video sequence with changing focus and offers a good approximation of the standard MTF. We examined the
effect of the 9 blur levels on iris recognition performance. Our results have shown that for moderately blurry
images (sharpness at least 50%) the drop in performance does not exceed 5% from the baseline (100% sharpness).
Low quality iris images such as blurry, low resolution images with poor illumination create a challenge
for iris recognition systems. Therefore, an efficient enhancement of iris images are needed in challenging
environments. We propose a new iris recognition algorithm for enhancement of normalized iris images. Our
algorithm is based on the logarithmic image processing (LIP) image enhancement which is used as one of the 3
stages in the enhancement process. Methods are tested on the MBGC database to compare enrolled video iris
images from 124 subjects with 220 pixels resolutions to query video portal images from 110 subjects with 120
pixels resolution. Results from processing challenging MBGC iris data show significant improvement in the
performance of iris recognition algorithms in terms of equal error rates compared to the original (unenhanced
images) and the other fast image enhancement methods.
Recent research has shown that it is possible to spoof a
variety of fingerprint scanners using some simple techniques with
molds made from plastic, clay, Play-Doh, silicon, or gelatin materials.
To protect against spoofing, methods of liveness detection measure
physiological signs of life from fingerprints, ensuring that only
live fingers are captured for enrollment or authentication. We propose
a new liveness detection method based on noise analysis
along the valleys in the ridge-valley structure of fingerprint images.
Unlike live fingers, which have a clear ridge-valley structure, artificial
fingers have a distinct noise distribution due to the material’s properties
when placed on a fingerprint scanner. Statistical features are
extracted in multiresolution scales using the wavelet decomposition
technique. Based on these features, liveness separation (live/
nonlive) is performed using classification trees and neural networks.
We test this method on the data set, that contains about 58 live, 80
spoof (50 made from Play-Doh and 30 made from gelatin), and 25
cadaver subjects for 3 different scanners. We also test this method
on a second data set that contains 28 live and 28 spoof (made from
silicon) subjects. Results show that we can get approximately 90.9–
100% classification of spoof and live fingerprints. The proposed liveness
detection method is purely software-based, and application of
this method can provide antispoofing protection for fingerprint
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.
Fingerprint scanners can be spoofed by fake fingers using moldable plastic, clay, Play-Doh, wax or gelatin. Liveness detection is an anti-spoofing method which can detect physiological signs of life from fingerprints to ensure only live fingers can be captured for enrollment or authentication. Our laboratory has demonstrated that the time-varying perspiration pattern can be used as a measure to detect liveness for fingerprint systems. Unlike spoof or cadaver fingers, live fingers have a distinctive spatial perspiration phenomenon both statically and dynamically. In this paper, a new
intensity based approach is presented which quantifies the grey level differences using histogram distribution statistics and finds distinct differences between live and non-live fingerprint images. Based on these static and dynamic features, we generate the decision rules to perform liveness classification. These methods were tested on optical, capacitive DC and electro-optical scanners using a dataset of about 58 live fingerprints, 50 spoof (made from Play-Doh and Gelatin) and 25 cadaver fingerprints. The dataset was divided into three sets: training set, validation set and test set. The training set was used to generate the classification tree model while the best tree model was decided by the validation set.
Finally, the test set was used to estimate the performance. The results are compared with the former ridge signal algorithm with new extracted features. The outcome shows that the intensity based approach and ridge signal approach can extract simple features which perform with excellent classification (about 90%~100%) for some scanners using a classification tree. The proposed liveness detection methods are purely software based, efficient and easy to be implemented for commercial use. Application of these methods can provide anti-spoofing protection for fingerprint scanners.
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.
Iris and face biometric systems are under intense study as a multimodal pair due in part to the ability to acquire both with the same capture system. While several successful research efforts have considered facial imagesas part of an iris-face multimodal biometric system, there is little work in the area exploring the iris recognition problem under different poses of the subjects. This is due to the fact that most commercial iris recognition systems depend on the high performance algorithm patented by Daugman, which does not take into consideration the pose and illumination variations in iris acquisition. Hence there is an impending need for sophisticated iris detection systems that localize the iris region for different poses and different facial views.
In this paper we present a non-frontal/non-ideal iris acquisition technique where iris images are extracted out of regular visual video sequences. This video sequence is captured 3 feet around the subject in a 90-degree arc from the profile view to the frontal view. We present a novel design for an iris detection filter that detects the location of the iris, the pupil and the sclera using a Laplacian of Gaussian ellipse detection technique. Experimental results show that the proposed approach can localize the iris location in facial images for a wide range of pose variations including semi-frontal views.
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.
Previous work in our laboratory and others have demonstrated that
spoof fingers made of a variety of materials including silicon,
Play-Doh, clay, and gelatin (gummy finger) can be scanned and
verified when compared to a live enrolled finger. Liveness, i.e.
to determine whether the introduced biometric is coming from a
live source, has been suggested as a means to circumvent attacks
using spoof fingers. We developed a new liveness method based on
perspiration changes in the fingerprint image. Recent results
showed approximately 90% classification rate using different
classification methods for various technologies including optical,
electro-optical, and capacitive DC, a shorter time window and a
diverse dataset. This paper focuses on improvement of the live
classification rate by using a weight decay method during the
training phase in order to improve the generalization and reduce
the variance of the neural network based classifier. The dataset
included fingerprint images from 33 live subjects, 33 spoofs
created with dental impression material and Play-Doh, and fourteen
cadaver fingers. 100% live classification was achieved with 81.8
to 100% spoof classification, depending on the device technology.
The weight-decay method improves upon past reports by increasing
the live and spoof classification rate.