A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

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An introduction to biometric recognition

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

/pdf/handbook-of-fingerprint-recognition-16mh887j24.pdf

Handbook of Fingerprint Recognition

Information fusion in biometrics

Establishing identity is becoming critical in our vastly interconnected society. Questions such as "Is she really who she claims to be?," "Is this person authorized to use this facility?," or "Is he in the watchlist posted by the government?" are routinely being posed in a variety of scenarios ranging from issuing a driver's license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual's identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometric scan solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems.

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Biometrics: a tool for information security

User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. Multimodal biometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. These systems also help achieve an increase in performance that may not be possible by using a single biometric indicator. This paper addresses the problem of information fusion in verification systems. Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are also presented.

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Information Fusion in Biometrics

SESAM: A biometric person identification system using sensor fusion

In the present paper we describe the person authentification system SESAM. Person identification and verification still is a very difficult task. Using one biometric feature, i.e. the photograph or the sound of the voice, leads to goods results, but there is no reliable way to verify the classification. In order to reach robust identification and verification we are combining three different biometric cues. These cues are dynamic, i.e. the sound of the voice and the lip motion, and static, i.e. the fixed image of the face. Each branch is preprocessed and classified separately and the results are combined, e.g. in a 2-from-3 manner. The recognition of persons may be used for pure identification or can be varied to a verification system. For both cases we have done a field test to show that this approach leads to a reliable person authentification system.

SESAM: A Biometric Person Identification System Using Sensor Fusion

Extending results of [HSS] on the construction of Neumann Laplacians of combs with given essential spectrum S = S [0,∞], we show that, in addition to the essential spectrum, a bounded sequence of discrete eigenvalues can be prescribed. More generally, we find that certain types of spectra can be constructed precisely, in a bounded interval [0, s].

Discrete and Cantor Spectrum for Neumann Laplacians of Combs

The system uses a mobile data detection platform (1) fitted to a vehicle and driven between the rows of parked cars. The platform possesses a sensor unit (10) with elements (infrared or ultrasonic) for sensing the position of a vehicle. A video camera is used to photograph the licence plate. The sensor unit has a stereo camera (20) for the right channel and another (21) for the left channel. Two ultrasonic sensors (23,24) to produce a stereo picture are connected by data lines (20a,21,a,23a) to the computing unit. Other video cameras (27), along with the stereo cameras (25,26), are added, for the normal and stereo pictures.

Parked cars licence plate detection system

The testing system comprises a control unit (1,2,3) with a multi-sine signal (MS) generator (20) for activating the loudspeakers (L,R;10r,11r,12r;101,111,121), receiving devices with one or two microphones (M1,M2;4,5) for measurement and processing of the tone-signals given out by the speakers, and a comparator (1,2) for comparison of the toner-signals with previously indicated and stored signals. The microphones may be directed symmetrically (100,M) or unsymmetrically (RE,LI) at the loudspeakers. If the tone signals match the comparison signals sufficiently closely, the speakers are functioning to a satisfactory standard, and if not, they may be faulty or incorrectly assembled. Thus detection is acoustically automated.

Peter Plankensteiner

Papers

SESAM: A biometric person identification system using sensor fusion

SESAM: A Biometric Person Identification System Using Sensor Fusion

Discrete and Cantor Spectrum for Neumann Laplacians of Combs

Parked cars licence plate detection system

Acoustic testing system for loudspeakers of stereo equipment