Aditya Abhyankar

On Techniques for Angle Compensation in Nonideal Iris Recognition

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word ldquononidealrdquo is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugmans integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

Integrating a wavelet based perspiration liveness check with fingerprint recognition

It has been shown that fingerprint scanners can be deceived very easily, using simple, inexpensive techniques. In this work, a countermeasure against such attacks is enhanced, that utilizes a wavelet based approach to detect liveness, integrated with the fingerprint matcher. Liveness is determined from perspiration changes along the fingerprint ridges, observed only in live people. The proposed algorithm was applied to a data set of approximately 58 live, 50 spoof and 28 cadaver fingerprint images captured at 0 and 2s, from each of three different types of scanners, for normal conditions. The results demonstrate perfect separation of live and not live for the normal conditions. Without liveness module the commercially available verifinger matcher is shown to give equal error rate (EER) of 13.85% where false reject rate is calculated for genuine-live users and false accept rate is for genuine-not live, imposter-live and imposter-not live. The integrated system of fingerprint matcher and liveness module reduces EER to 0.03%. Results are also presented for moist and dry fingers simulated by glycerin and acetone, respectively. The system is further tested using gummy fingers and various deliberately simulated conditions including pressure change and adding moisture to the spoof to analyze the strength of the liveness algorithm.

Iris quality assessment and bi-orthogonal wavelet based encoding for recognition

Iris recognition has been demonstrated to be an efficient technology for personal identification. In this work, methods to perform iris encoding using bi-orthogonal wavelets and directional bi-orthogonal filters are proposed and compared. All the iris images are enhanced using the wavelet domain in-band de-noising method. This method is shown to improve the iris segmentation results. A framework to assess the iris image quality based on occlusion, contrast, focus and angular deformation is introduced and used as part of a novel adaptive matching technique based on the assessed iris image quality. Adaptive matching presents improved performance when compared against the Hamming distance method. Four different databases are used to analyze the system performance. The first two databases include popular CASIA and high resolution University of Bath databases. Results obtained for these databases compare with results from the literature, in terms of speed as well as accuracy. The other two databases have challenging off-angle (WVU database) and uncontrolled (Clarkson database) iris images and are used to assess the limits of system performance. Best results are achieved for directional bi-orthogonal filter based encoding technique combined with the adaptive matching method with EER values of 0.07%, 0.15%, 0.81% and 1.29% for the four databases, which reflect highly competent performance and high correlation with the quality of the iris images.

Active shape models for effective iris segmentation

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.

A novel biorthogonal wavelet network system for off-angle iris recognition

One important category of non-ideal conditions for iris recognition is off-angle iris images. Practically it is very difficult for images to be captured with no offset. It then becomes necessary to account for off angle information in order to maintain robust performance. A biorthogonal wavelet based iris recognition system, previously designed at our lab, is modified and demonstrated to perform off-angle iris recognition. Biorthogonal wavelet network (BWN) are developed and trained for each class. The non-ideal factors are adjusted by repositioning the BWN. To test, along with the real data, synthetic iris images are generated by using affine and geometric transforms of 0^o, 10^o and 20^@? experimentally collected images. The tests were carried out on the experimentally collected off-angle data and synthetically generated data for angles from 0^o to 60^@? with a resolution of 5^@?. This approach is shown to have less constraints than a transformation based iris recognition approach. Iris images off-angle by up to 42^@? for synthetic data and up to 45^@? for experimental data are successfully recognized.

Detecting Liveness in Fingerprint Scanners Using Wavelets: Results of the Test Dataset

A novel method is proposed to detect “liveness” associated with fingerprint devices. The physiological phenomenon of perspiration, observed only in live people, is used as a measure to classify ‘live’ fingers from ‘not live’ fingers. Pre-processing involves filtering of the images using different image processing techniques. Wavelet analysis of the images is performed using Daubechies wavelet. Multiresolution analysis is performed to extract information from the low frequency content, while wavelet packet analysis is performed to analyze the high frequency information content. A threshold is applied to the first difference of the information in all the sub-bands. The energy content of the changing wavelet coefficients, which are directly associated with the perspiration pattern, is used as a quantified measure to differentiate live fingers from others. The proposed algorithm was applied to a data set of approximately 30 live, 30 spoof and 14 cadaver fingerprint images from three different types of scanners. The algorithm was able to completely classify ‘live’ fingers from ‘not live’ fingers providing a method for improved spoof protection.

Off-angle iris recognition using bi-orthogonal wavelet network system

One important category of non-ideal conditions for iris recognition is off-angle iris images. Practically it is very difficult for images to be captured with no offset. It then becomes necessary to account for off angle information in order to maintain robust performance. A bi-orthogonal wavelet based iris recognition system, previously designed at our lab, is modified and demonstrated to perform off-angle iris recognition. Bi-orthogonal wavelet network (BWN) are developed and trained for each class. The non-ideal factors are adjusted by repositioning the BWN. To test, synthetic iris images are generated by using affine and geometric transforms of 0/spl deg/, 10/spl deg/ and 20/spl deg/ experimentally collected images from 101 subjects. This approach is shown to perform better than a transformation based iris recognition approach. Iris images off-angle by up to 42/spl deg/ are successfully recognized.

Novel Biorthogonal Wavelet based Iris Recognition for Robust Biometric System

—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 Daugmans 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.

Towards integrating level-3 Features with perspiration pattern for robust fingerprint recognition

Level-3 fingerprint features from fingerprint images like pores are difficult to capture detect, and involve high resolution scanners with higher ppi count. However, these features provide finer information about a fingerprint characteristics. Furthermore, fingerprint pores may be useful in determining liveness of fingerprint in order to prevent spoofing of fingerprint devices. In this study fingerprint pores along the ridges are used for fingerprint matching. Wavelet based fingerprint enhancement techniques are implemented to ease detection of the level-3 features. Delaunay triangulation based alignment and matching of the fingerprints is performed. The pores are checked for the liveness by perspiration activity in the time series captures. The developed matching scheme is tested for the high resolution data (686 ppi) for 114 live and spoof fingerprint classes. ROC is plotted and EER of 2.97% is obtained.

Biorthogonal-wavelets-based iris recognition

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 Daugmans 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.