Shreyas Venugopalan

This book chapter explores the possibility of recovering iris images from binary iris templates. It has been generally assumed that the binary iris code is irreversible, i.e., the original iris texture cannot be derived from it. Here, we discuss two distinct approaches to reconstruct the iris texture from the binary iris code. Next, we discuss a method to detect such synthesized iris textures. Finally, we discuss some of the advantages and risks of generating iris texture from iris codes in the… Show more

This book chapter explores the possibility of recovering iris images from binary iris templates. It has been generally assumed that the binary iris code is irreversible, i.e., the original iris texture cannot be derived from it. Here, we discuss two distinct approaches to reconstruct the iris texture from the binary iris code. Next, we discuss a method to detect such synthesized iris textures. Finally, we discuss some of the advantages and risks of generating iris texture from iris codes in the context of data privacy and security. Show less

This paper presents a novel iris super-resolution approach using a powerful nonparametric Bayesian modeling technique in the framework of sparse representation and over-complete dictionary. Far apart from previous iris super-resolution methods, our proposed approach has ability to automatically discover optimal parameter sets and optimally adapt from a given training data. Particularly, the Beta Process will be employed to build a nonparametric discriminative over-complete dictionary to… Show more

This paper presents a novel iris super-resolution approach using a powerful nonparametric Bayesian modeling technique in the framework of sparse representation and over-complete dictionary. Far apart from previous iris super-resolution methods, our proposed approach has ability to automatically discover optimal parameter sets and optimally adapt from a given training data. Particularly, the Beta Process will be employed to build a nonparametric discriminative over-complete dictionary to represent and discriminate input samples simultaneously. Our proposed method will be evaluated on Casia iris database and compared with the linear interpolation super resolution. The result shows that our approach improves the performance of iris recognition. Show less

Biometrics has come a long way over the past decade in terms of technologies and devices that are used to verify user identities. Three of the more well studied modalities in this field are the face, iris and fingerprint, with the latter two reporting very high user identification/verification rates. In the biometric community there has been little work in studying biomedical signals for user recognition purposes. In this paper, we propose using electromyograph (EMG) signals as a person's… Show more

Biometrics has come a long way over the past decade in terms of technologies and devices that are used to verify user identities. Three of the more well studied modalities in this field are the face, iris and fingerprint, with the latter two reporting very high user identification/verification rates. In the biometric community there has been little work in studying biomedical signals for user recognition purposes. In this paper, we propose using electromyograph (EMG) signals as a person's biometric signature. The EMG records the motor unit action potentials (MUAP) during any physical motion. Our study is done within the context of a person using a keyboard to type a password or any other fixed phrase. Along with EMG signals, we log key press times for the user and study the feasibility of using this data too as a biometric feature. Keypress timings alone if used as a biometric, are very easy to spoof and hence we fuse this modality with EMG signals. In order to classify these features, we use subspace modeling as well as Bayesian classifiers. The experiments have been performed within the context of a user typing a fixed pass phrase at a workstation. The idea is to monitor both biometric modalities when this action is performed and study user verification across data capture sessions and within capture sessions. Our approach yields high values of verification rates, which shows the promise of using these modalities as user specific biometric signatures. Show less

Over the past two decades, there have been a lot of advances in the field of pattern analyses for biomedical signals, which have helped in both medical diagnoses and in furthering our understanding of the human body. A relatively recent area of interest is the utility of biomedical signals in the field of biometrics i.e. for user identification. Seminal work in this domain has already been done using electrocardiograph (ECG) signals. In this article, we discuss our ongoing work in using a… Show more

Over the past two decades, there have been a lot of advances in the field of pattern analyses for biomedical signals, which have helped in both medical diagnoses and in furthering our understanding of the human body. A relatively recent area of interest is the utility of biomedical signals in the field of biometrics i.e. for user identification. Seminal work in this domain has already been done using electrocardiograph (ECG) signals. In this article, we discuss our ongoing work in using a relatively recent modality of biomedical signals - a cardio-synchronous waveform measured using a Radio Frequency Impedance Interrogation(RFII) device for the purpose of user identification. Compared to an ECG setup, this device is noninvasive and measurements can be obtained easily and quickly. Here we discuss the feasibility of reducing the dimensions of these signals by projecting onto various sub-spaces while still preserving inter-user discriminating information. We compare the classification performance using classical dimensionality reduction methods such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), random projections, with more recent techniques such as K-SVD based dictionary learning. We also report the reconstruction accuracies in these sub-spaces. Our results show that the dimensionality of the measured signals can be reduced by 60 fold while maintaining high user identification rates. Show less

In this paper we explore how a Radio Frequency Impedance Interrogation (RFII) signal may be used as a biometric feature. This could allow the identification of subjects in operational and potentially hostile environments. Features extracted from the continuous and discrete wavelet decompositions of the signal are investigated for biometric identification. In the former case, the most discriminative features in the wavelet space were extracted using a Fisher ratio metric. Comparisons in the… Show more

In this paper we explore how a Radio Frequency Impedance Interrogation (RFII) signal may be used as a biometric feature. This could allow the identification of subjects in operational and potentially hostile environments. Features extracted from the continuous and discrete wavelet decompositions of the signal are investigated for biometric identification. In the former case, the most discriminative features in the wavelet space were extracted using a Fisher ratio metric. Comparisons in the wavelet space were done using the Euclidean distance measure. In the latter case, the signal was decomposed at various levels using different wavelet bases, in order to extract both low frequency and high frequency components. Comparisons at each decomposition level were performed using the same distance measure as before. The data set used consists of four subjects, each with a 15 minute RFII recording. The various data samples for our experiments, corresponding to a single heart beat duration, were extracted from these recordings. We achieve identification rates of up to 99% using the CWT approach and rates of up to 100% using the DWT approach. While the small size of the dataset limits the interpretation of these results, further work with larger datasets is expected to develop better algorithms for subject identification. Show less

Biometric systems based on iridal patterns have shown very high accuracies in verifying an individual’s identity due to the uniqueness of the iris pattern across individuals. For identity verification purposes, only the iris bit code template of an individual need be stored. In this chapter, we explore methods to generate synthetic iris textures corresponding to a given person for the purpose of bypassing an iris-based security system using these iris templates. We present analysis to prove… Show more

Biometric systems based on iridal patterns have shown very high accuracies in verifying an individual’s identity due to the uniqueness of the iris pattern across individuals. For identity verification purposes, only the iris bit code template of an individual need be stored. In this chapter, we explore methods to generate synthetic iris textures corresponding to a given person for the purpose of bypassing an iris-based security system using these iris templates. We present analysis to prove that when this “spoof” texture is presented to an iris recognition system; it will elicit a similar response from the system as that due to the genuine iris texture to which the spoof corresponds. We embed this spoof texture within the iris of an imposter to achieve this end. Systems using filter-based feature extraction systems – such as Daugman style systems – may be bypassed using this technique. We assume knowledge of solely the feature extraction mechanism of the iris matching scheme and, as mentioned, the iris bit code template of the person whose iris is to be spoofed. We present a complete investigation into how one can get by an iris recognition system using this approach, by generating various “natural”-looking irises and hope to use this knowledge to incorporate several countermeasures into the feature extraction scheme of an iris recognition module. Show less

Most iris based biometric systems require a lot of co- operation from the users so that iris images of acceptable quality may be acquired. Features from these may then be used for recognition purposes. Relatively fewer works in literature address the question of less cooperative iris acquisition systems in order to reduce constraints on users. In this paper, we describe our ongoing work in designing and developing such a system. It is capable of capturing images of the iris up to distances of 8… Show more

Most iris based biometric systems require a lot of co- operation from the users so that iris images of acceptable quality may be acquired. Features from these may then be used for recognition purposes. Relatively fewer works in literature address the question of less cooperative iris acquisition systems in order to reduce constraints on users. In this paper, we describe our ongoing work in designing and developing such a system. It is capable of capturing images of the iris up to distances of 8 meters with a resolution of 200 pixels across the diameter. If the resolution requirement is decreased to 150 pixels, then the same system may be used to capture images from up to 12 meters. We have incorporated velocity estimation and focus tracking modules so that images may be acquired from subjects on the move as well. We describe the various components that make up the system, including the lenses used, the imaging sensor, our auto-focus function and velocity estimation module. All the hardware components are Commercial Off The Shelf (COTS) with little or no modifications. We also present preliminary iris acquisition results using our system for both stationary and mobile subjects. Show less

Biometrics has gained a lot of attention over recent years as a way to identify individuals. Of all biometrics-based techniques, the iris-pattern-based systems have recently shown very high accuracies in verifying an individual's identity. The premise here is that iris patterns are unique across people. Only the iris bit code template specific to an individual need be stored for future identity verification. It is generally accepted that this iris bit code is unidentifiable data. However, in… Show more

Biometrics has gained a lot of attention over recent years as a way to identify individuals. Of all biometrics-based techniques, the iris-pattern-based systems have recently shown very high accuracies in verifying an individual's identity. The premise here is that iris patterns are unique across people. Only the iris bit code template specific to an individual need be stored for future identity verification. It is generally accepted that this iris bit code is unidentifiable data. However, in this work, we explore methods to generate alternate iris textures for a given person for the purpose of bypassing a system based on this iris bit code. We show that, if this spoof texture is presented to an iris recognition system, it will generate the same score response as that of the original iris texture. Hence, this approach can bypass filter-based feature extraction systems (such as Daugman style systems) without using the actual texture of the target iris that we want to spoof, by obtaining a hamming distance match score that falls within the authentic score range. This approach assumes we know the feature extraction mechanism of the iris matching scheme. We embed features within a person's natural iris texture to spoof another person's iris. A very convincing preliminary investigation into how one can get by any iris recognition system by synthesizing various levels of “natural” looking irises is presented here and we hope to use this knowledge to build countermeasures into the feature extraction scheme of the recognition module. Show less

In this paper, we perform a detailed investigation of various features that can be extracted from the periocular region of human faces for biometric identification. The emphasis of this study is to explore the BEST feature extraction approach used in stand-alone mode without any generative or discriminative subspace training. Simple distance measures are used to determine the verification rate (VR) on a very large dataset. Several filter-based techniques and local feature extraction methods are… Show more

In this paper, we perform a detailed investigation of various features that can be extracted from the periocular region of human faces for biometric identification. The emphasis of this study is to explore the BEST feature extraction approach used in stand-alone mode without any generative or discriminative subspace training. Simple distance measures are used to determine the verification rate (VR) on a very large dataset. Several filter-based techniques and local feature extraction methods are explored in this study, where we show an increase of 15% verification performance at 0.1% false accept rate (FAR) compared to raw pixels with the proposed Local Walsh-Transform Binary Pattern encoding. Additionally, when fusing our best feature extraction method with Kernel Correlation Feature Analysis (KCFA), we were able to obtain VR of 61.2%. Our experiments are carried out on the large validation set of the NIST FRGC database, which contains facial images from environments with uncontrolled illumination. Verification experiments based on a pure 1–1 similarity matrix of 16028×8014 (~128 million comparisons) carried out on the entire database, where we find that we can achieve a raw VR of 17.0% at 0.1% FAR using our proposed Local Walsh-Transform Binary Pattern approach. This result, while may seem low, is more than the NIST reported baseline VR on the same dataset (12% at 0.1% FAR), when PCA was trained on the entire facial features for recognition. Show less

Uniqueness of iris patterns among individuals has resulted in the ubiquity of iris recognition systems in virtual and physical spaces, at high security facilities around the globe. Traditional methods of acquiring iris patterns in commercial systems scan the iris when an individual is at a predetermined location in front of the scanner. Most state-of-the-art techniques for unconstrained iris acquisition in literature use expensive customequipment and are composed of amulticamera setup, which is… Show more

Uniqueness of iris patterns among individuals has resulted in the ubiquity of iris recognition systems in virtual and physical spaces, at high security facilities around the globe. Traditional methods of acquiring iris patterns in commercial systems scan the iris when an individual is at a predetermined location in front of the scanner. Most state-of-the-art techniques for unconstrained iris acquisition in literature use expensive customequipment and are composed of amulticamera setup, which is bulky, expensive, and requires calibration. This paper investigates a method of unconstrained iris acquisition and recognition using a single commercial off-the-shelf (COTS) pan-tilt-zoom (PTZ) camera, that is compact and that reduces the cost of the final system, compared to other proposed hierarchical multicomponent systems. We employ state-of-the-art techniques for face detection and a robust eye detection scheme using active shape models for accurate landmark localization. Additionally, our system alleviates the need for any calibration stage prior to its use. We present results using a database of iris images captured using our system, while operating in an unconstrained acquisition mode at 1.5m standoff, yielding an iris diameter in the 150-200 pixels range. Show less