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.

In this talk, we define "multi-biometrics" and "multi-modal" biometrics and discuss the reasons why "multi-modal" biometrics has failed to achieve its promise, despite 30 years of research. We give examples of how "multi-biometric" systems have been successfully used in large-scale applications, such as the Australian "SmartGate", US-VISIT and U.K. IRIS systems, and present a Bayesian-based, parameter-free approach to combining multi-biometric data

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.

In this context, "biometrics" is the "automatic identification or identity verification of living, human individuals based on physiological and behavioral characteristics". Biometrics is a subset of the larger field of human identification science. The term "automatic" in the definition means that digital computers will usually (but not always) be used. We consider the general state of digital signal processing in biometrics by examining the computational approaches to speaker, fingerprint, and face recognition. We note the generally weak relationship between physiology and biometric "features" and the potential for use of "models" to compensate for intra-class variation.

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.

The author discusses the use of simulated annealing for optimizing signal distribution networks consisting of multiplexers, switches, buses and end-devices or users. A linear algebraic approach is developed for application of simulated annealing to problems of this kind. The author shows how such an approach allows for the optimization of general capacity-constrained signal distribution networks containing multiple levels of demultiplexers, switches, and buses, with linear and nonlinear objective functions. Examples include both linear and nonlinear objective functions.>