Biometric Authentication: International ECCV 2002 Workshop by O. Déniz, M. Castrillón, J. Lorenzo, M. Hernández (auth.),

By O. Déniz, M. Castrillón, J. Lorenzo, M. Hernández (auth.), Massimo Tistarelli, Josef Bigun, Anil K. Jain (eds.)

Biometric authentication refers to opting for someone in response to his or her distinguishing physiological and/or behavioral features. It affiliates a person with a formerly decided id in line with that specific s visual appeal or habit. simply because many physiological or behavioral features (biometric symptoms) are detailed to every individual, biometric identifiers are inherently extra trustworthy and extra able than knowledge-based (e.g., password) and token-based (e.g., a key) concepts in differentiating among a licensed individual and a fraudulent impostor. consequently, increasingly more companies want to computerized identification authentication structures to enhance consumer delight, protection, and working potency in addition to to avoid wasting serious assets. Biometric authentication is a tough trend reputation challenge; it contains greater than simply template matching. The intrinsic nature of biometric information has to be conscientiously studied, analyzed, and its houses taken into consideration in constructing appropriate illustration and matching algorithms. The intrinsic variability of knowledge with time and environmental stipulations, the social acceptability and invasiveness of acquisition units, and the power with which the information should be counterfeited needs to be thought of within the selection of a biometric indicator for a given software. so one can install a biometric authentication process, one needs to ponder its reliability, accuracy, applicability, and potency. ultimately, it can be essential to mix numerous biometric symptoms (multimodal-biometrics) to deal with the drawbacks of the person biometric indicators.

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5] C. Beumier and M. Acheroy. Automatic 3d face authentication. Image and Vision Computing, 18(4), 2000. [6] M. Proesmans and L. Van Gool. One shot 3d-shape and texture acquisition of facial data. In Audio and Video based Person Authentication - AVBPA97. Springer, 1997. [7] R. Brunelli and T. Poggio. Face recognition: Features versus templates. IEEE Trans. on PAMI, PAMI-15(10):1042–1052, 1993. [8] I. Biederman. Human image understanding: Recent research and theory. CVGIP, 32:29–73, 1985. [9] L.

Table 2. Error indices computed from the industrial database by using feature templates obtained by averaging several images (Template A) or extracted directly from the subject’s image to be matched against the database (Template B). 4% The accuracy in the localization of the facial features can be improved further by using a log-polar feature template, at the expense of an added computational cost. As already demonstrated, the space-variant topology of the log-polar transform has the consequence of locally reducing the amplitude of the correlation function.

Com Abstract. We present a novel fingerprint alignment and matching scheme that utilizes ridge feature maps to represent, align and match fingerprint images. The technique described here obviates the need for extracting minutiae points or the core point to either align or match fingerprint images. The proposed scheme examines the ridge strength (in local neighborhoods of the fingerprint image) at various orientations, using a set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints.

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