James L. Wayman

FVC2000: fingerprint verification competition

Reliable and accurate fingerprint recognition is a challenging pattern recognition problem, requiring algorithms robust in many contexts. FVC2000 competition attempted to establish the first common benchmark, allowing companies and academic institutions to unambiguously compare performance and track improvements in their fingerprint recognition algorithms. Three databases were created using different state-of-the-art sensors and a fourth database was artificially generated; 11 algorithms were extensively tested on the four data sets. We believe that FVC2000 protocol, databases, and results will be useful to all practitioners in the field not only as a benchmark for improving methods, but also for enabling an unbiased evaluation of algorithms.

FVC2002: Second Fingerprint Verification Competition

Two years after the first edition, a new Fingerprint Verification Competition (FVC2002) was organized by the authors, with the aim of determining the state-of-the-art in this challenging pattern recognition application. The experience and the feedback received from FVC2000 allowed the authors to improve the organization of FVC2002 and to capture the attention of a significantly higher number of academic and commercial organizations (33 algorithms were submitted). This paper discusses the FVC2002 database, the test protocol and the main differences between FVC2000 and FVC2002. The algorithm performance evaluation will be presented at the 16/sup th/ ICPR.

Performance evaluation of fingerprint verification systems

This paper is concerned with the performance evaluation of fingerprint verification systems. After an initial classification of biometric testing initiatives, we explore both the theoretical and practical issues related to performance evaluation by presenting the outcome of the recent fingerprint verification competition (FVC2004). FVC2004 was organized by the authors of this work for the purpose of assessing the state-of-the-art in this challenging pattern recognition application and making available a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems. FVC2004 is an independent, strongly supervised evaluation performed at the evaluators site on evaluators hardware. This allowed the test to be completely controlled and the computation times of different algorithms to be fairly compared. The experience and feedback received from previous, similar competitions (FVC2000 and FVC2002) allowed us to improve the organization and methodology of FVC2004 and to capture the attention of a significantly higher number of academic and commercial organizations (67 algorithms were submitted for FVC2004). A new, light competition category was included to estimate the loss of matching performance caused by imposing computational constraints. This paper discusses data collection and testing protocols, and includes a detailed analysis of the results. We introduce a simple but effective method for comparing algorithms at the score level, allowing us to isolate difficult cases (images) and to study error correlations and algorithm fusion. The huge amount of information obtained, including a structured classification of the submitted algorithms on the basis of their features, makes it possible to better understand how current fingerprint recognition systems work and to delineate useful research directions for the future.

FVC2004: Third Fingerprint Verification Competition

A new technology evaluation of fingerprint verification algorithms has been organized following the approach of the previous FVC2000 and FVC2002 evaluations, with the aim of tracking the quickly evolving state-of-the-art of fingerprint recognition systems. Three sensors have been used for data collection, including a solid state sweeping sensor, and two optical sensors of different characteristics. The competition included a new category dedicated to ”light” systems, characterized by limited computational and storage resources. This paper summarizes the main activities of the FVC2004 organization and provides a first overview of the evaluation. Results will be further elaborated and officially presented at the International Conference on Biometric Authentication (Hong Kong) on July 2004.

Automatic Eye Detection and Its Validation

The accuracy of face alignment affects the performance of a face recognition system. Since face alignment is usually conducted using eye positions, an accurate eye localization algorithm is therefore essential for accurate face recognition. In this paper, we first study the impact of eye locations on face recognition accuracy, and then introduce an automatic technique for eye detection. The performance of our automatic eye detection technique is subsequently validated using FRGC 1.0 database. The validation shows that our eye detector has an overall 94.5% eye detection rate, with the detected eyes very close to the manually provided eye positions. In addition, the face recognition performance based on the automatic eye detection is shown to be comparable to that of using manually given eye positions.

FUNDAMENTALS OF BIOMETRIC AUTHENTICATION TECHNOLOGIES

Biometric authentication technologies are used for the machine identification of individuals. The human-generated patterns used may be primarily physiological or behavioral, but usually contain elements of both components. Examples include voice, handwriting, face, eye and fingerprint identification. In this paper, we look at these technologies and their applications in general, developing a systematic approach to classifying, analyzing and evaluating them. A general system model is shown and test results for a number of technologies are considered.

Technical Testing and Evaluation of Biometric Identification Devices

Although the technical evaluation of biometric identification devices has a history spanning over two decades, it is only now that a general consensus on test and reporting measures and methodologies is developing in the scientific community. By “technical evaluation”, we mean the measurement of the five parameters generally of interest to engineers and physical scientists: false match and false non-match rates, binning error rate, penetration coefficient and transaction times. Additional measures, such as “failure to enroll” or “failure to acquire”, indicative of the percentage of the general population unable to use any particular biometric method, are also important. We have not included in this chapter measures of more interest to social scientists, such as user perception and acceptability. Most researchers now accept the “Receiver Operating Characteristic” (ROC) curve as the appropriate measure of the application-dependent technical performance of any biometric identification device. Further, we now agree that the error rates illustrated in the ROC must be normalized to be independent of the database size and other “accept/reject” decision parameters of the test. This chapter discusses the general approach to application-dependent, decision-policy independent testing and reporting of technical device performance and gives an example of one practical test. System performance prediction based on test results is also discussed.

Error rate equations for the general biometric system

We derive equations for false-match and false-nonmatch error-rate prediction for the general M-to-N biometric identification system, under the simplifying, but limiting, assumption of statistical independence of all errors. For systems with large N, error rates are shown to be linked to the hardware processing speed through the system penetration coefficient and the throughput equation. These equations are somewhat limited in their ability to handle sample-dependent decision policies and are shown to be consistent with previously published cases for verification and identification. Applying parameters consistent with the Philippine Social Security System benchmark test results for AFIS vendors, we establish that biometric identification systems can be used in populations of 100 million people. Development of more generalized equations, accounting for error correlation and general sample-dependent thresholds, establishing confidence bounds, and substituting the inter-template for the impostor distribution under the template generating policy remain for future study.

Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores

This paper presents methods of modeling and predicting face recognition (FR) system performance based on analysis of similarity scores. We define the performance of an FR system as its recognition accuracy, and consider the intrinsic and extrinsic factors affecting its performance. The intrinsic factors of an FR system include the gallery images, the FR algorithm, and the tuning parameters. The extrinsic factors include mainly query image conditions. For performance modeling, we propose the concept of perfect recognition, based on which a performance metric is extracted from perfect recognition similarity scores (PRSS) to relate the performance of an FR system to its intrinsic factors. The PRSS performance metric allows tuning FR algorithm parameters offline for near optimal performance. In addition, the performance metric extracted from query images is used to adjust face alignment parameters online for improved performance. For online prediction of the performance of an FR system on query images, features are extracted from the actual recognition similarity scores and their corresponding PRSS. Using such features, we can predict online if an individual query image can be correctly matched by the FR system, based on which we can reduce the incorrect match rates. Experimental results demonstrate that the performance of an FR system can be significantly improved using the presented methods

Biometrics in Identity Management Systems

Biometric technology - the automated recognition of individuals using biological and behavioral traits - has been presented as a natural identity management tool that offers greater security and convenience than traditional methods of personal recognition. Indeed, many existing government identity management systems employ biometrics to assure that each person has only one identity in the system and that only one person can access each identity. Historically, however, biometric technology has also been controversial, with many writers suggesting that biometrics invade privacy, that specific technologies have error rates unsuitable for large-scale applications, or that the techniques are useful to organizations that regulate the individual, but of little use where the individual controls identification and authorization. Here, I address these controversies by looking more deeply into the basic assumptions made in biometric recognition. Ill look at some example systems and delve into the differences between personal identity and digital identity. Ill conclude by discussing how those whose identity is managed with biometrics can manage biometric identity management.