Download e-book for kindle: Advanced Studies in Biometrics: Summer School on Biometrics, by J. Bigun, J. Fierrez-Aguilar, J. Ortega-Garcia, J.

Download e-book for kindle: Advanced Studies in Biometrics: Summer School on Biometrics, by J. Bigun, J. Fierrez-Aguilar, J. Ortega-Garcia, J.

By J. Bigun, J. Fierrez-Aguilar, J. Ortega-Garcia, J. Gonzalez-Rodriguez (auth.), Massimo Tistarelli, Josef Bigun, Enrico Grosso (eds.)

Automatic individual authentication, the identity and verification of somebody as such, has more and more been said as an important point of varied protection functions. a number of popularity and id structures were in keeping with biometrics using biometric positive aspects reminiscent of fingerprint, face, retina scans, iris styles, hand geometry, DNA lines, gait, and others.

This e-book originates from a world summer time university on biometrics, held in Alghero, Italy, in June 2003. The seven revised educational lectures through major researchers introduce the reader to biometrics-based individual authentication, fingerprint attractiveness, gait reputation, a number of elements of face acceptance and face detection, topologies for biometric attractiveness, and hand detection. additionally incorporated are the 4 most sensible chosen pupil papers, all facing face recognition.

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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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Extra resources for Advanced Studies in Biometrics: Summer School on Biometrics, Alghero, Italy, June 2-6, 2003. Revised Selected Lectures and Papers

Example text

Boyd [41] tested the phase-locked loops for the ability to recognize individual people using the shape-of-motion database [37] and the MoBo database [23]. With shape-of-motion data, recognition was perfect, 100% with sample size six. Using the MoBo database recognition rates were between 47% and 91% depending on whether or not sequences portraying the same style of gait were allowed to match. Boyd also observed that ignoring the phase information lowered the recognition rate in all cases. In related work, Liu and Picard [44] Polana and Nelson [45] also look pixel level variations to analyze cyclic motions, however, they do not apply their analysis to biometric recognition.

For example, see the trends in the plots in Bhanu and Han [6] and Ben-Abdelkader et al. [33]. Intuitively, this occurs because the larger the sample, the more opportunities there are to make a mistake. In terms of the features used for recognition, as the sample size increases, the feature space becomes crowded, thus providing less resolution between individuals. In spite of their intuitive appeal, recognition rates must be considered only within the context in which they are produced. Failure to consider any of the above factors in comparing recognition rates will almost certainly lead to false conclusions.

Summing orientation differences requires a direction (among the two possible) to be associated at each orientation. A solution to this problem is to randomly select the direction of the first element and assign the direction closest to that of the previous element to each successive element. d2 d3 d1 [i,j] d0 d7 d4 d2 d5 d3 d4 d1 [i,j] d5 d6 PG,C(i,j) = 360° d0 d7 d6 PG,C(i,j) = 180° d2 d3 d4 d1 [i,j] d7 d0 d5 d6 PG,C(i,j) = -180° Fig. 11. Examples of computation of the Poincaré index in the 8-neighborhood of points belonging (from the left to the right) to a whorl, loop, and delta singularity, respectively.

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