By J. Bigun, J. Fierrez-Aguilar, J. Ortega-Garcia, J. Gonzalez-Rodriguez (auth.), Massimo Tistarelli, Josef Bigun, Enrico Grosso (eds.)
Automatic individual authentication, the id and verification of somebody as such, has more and more been said as an important element of assorted defense purposes. quite a few reputation and id structures were in response to biometrics using biometric beneficial properties reminiscent of fingerprint, face, retina scans, iris styles, hand geometry, DNA strains, gait, and others.
This publication originates from a global summer time university on biometrics, held in Alghero, Italy, in June 2003. The seven revised instructional lectures through major researchers introduce the reader to biometrics-based individual authentication, fingerprint reputation, gait acceptance, numerous features of face acceptance and face detection, topologies for biometric acceptance, and hand detection. additionally integrated are the 4 top chosen pupil papers, all facing face recognition.
Read Online or Download Advanced Studies in Biometrics: Summer School on Biometrics, Alghero, Italy, June 2-6, 2003. Revised Selected Lectures and Papers PDF
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Additional resources for Advanced Studies in Biometrics: Summer School on Biometrics, Alghero, Italy, June 2-6, 2003. Revised Selected Lectures and Papers
Jain and S. Prabhakar, “Handbook of Fingerprint Recognition,” Springer, 2003. 2003 Springer. M. Tistarelli, J. Bigun, and E. ): Biometrics School 2003, LNCS 3161, pp. 43-68, 2005. Springer-Verlag Berlin Heidelberg 2005 44 D. Maltoni tractor to generate a compact but expressive representation, called a template. The verification task is responsible for verifying individuals at the point of access. During the operation phase, the user’s name or PIN (Personal Identification Number) is entered through a keyboard (or a keypad); the biometric reader captures the fingerprint of the individual to be recognized and converts it to a digital format, which is further processed by the feature extractor to produce a compact digital representation.
5 shows Biometric Gait Recognition 27 an example of optical ﬂow computed using the region-matching algorithm of Bulthoﬀ et al. . (a) (b) (c) Fig. 5. Example of Bulthoﬀ et al.  region-matching optical ﬂow: (a) original image from a sequence, and (b) x- and (c) y-direction optical ﬂow. In (b) and (c) gray is zero, black is negative (left/up), and white is positive (right/down). 4 Motion Energy and Motion History Images Davis and Bobick  describe a motion energy image (MEI) and a motion history image (MHI), both derived from temporal image sequences.
8. Performance comparison of biometric gait recognition systems showing recognition rate versus sample size. The curve labeled Random indicates the expected recognition rate for random guesses. CK(77) refers to Cutting and Kozlowski , BH(02)a and BH(02)b refer to Bhanu and Han  5mm and 40mm resolution respectively, LB(98) refers to Little and Boyd , BCND(01) refers to Ben-Abdelkader et al. , SN(01) refers to Shutler and Nixon , TB(01) refers to Tanawongsuwan and Bobick , CNC(03) refers to Cunado et al.