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 someone as such, has more and more been stated as an important element of varied defense purposes. numerous popularity and identity platforms were in line with biometrics using biometric gains corresponding to fingerprint, face, retina scans, iris styles, hand geometry, DNA strains, gait, and others.
This e-book originates from a world summer season university on biometrics, held in Alghero, Italy, in June 2003. The seven revised educational lectures by means of top researchers introduce the reader to biometrics-based individual authentication, fingerprint popularity, gait reputation, a variety of elements of face popularity and face detection, topologies for biometric attractiveness, and hand detection. additionally integrated are the 4 top chosen pupil papers, all facing face recognition.
Read 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 info for Advanced Studies in Biometrics: Summer School on Biometrics, Alghero, Italy, June 2-6, 2003. Revised Selected Lectures and Papers
Little ... ,sm) φm ... F2 = φ2 − φm Fm−1 = φm−1 − φm (F1,F2, ... ,Fm−1) feature vector Fig. 9. The shape-of-motion gait recognition system . 300 6 250 4 200 2 X of Centroid X of Centroid X of Centroid Fit to X of Centroid 150 0 100 -2 50 -4 0 -6 10 20 30 40 Time (a) 50 60 70 80 10 20 30 40 Time 50 60 70 80 (b) Fig. 10. Sample data from the shape-of-motion system: (a) a x-coordinate sequence, and (b) the sequence with the non-oscillatory component removed and a ﬁtted sinusoid at the measured frequency and phase.
2 29 Recognition Rate Estimating the rate of correct recognition for a gait biometric has an intuitive appeal. It seems natural to think of system performance in terms of how often the system gets it right. To arrive at such estimates, the procedure is to take a sample of the population of interest. One then divides the sample into two partitions, one for training the system (the database in Fig. 7), and one for testing. The estimated rate of correct recognition is the fraction of the test set that the system classiﬁes correctly.
Moreover, it takes a few seconds to acquire an image. Hence, this technology is not yet mature enough for large-scale production. Table 1 lists some commercial scanners designed for the non-AFIS markets, whose cost is less than $200 US. Except for ultrasound scanners, which are not ready for mass-market applications yet, Table 1 includes at least one scanner for each technology. Examples of the same fingerprint (from a good-quality finger, a dry finger, a wet finger, and a poor quality finger, respectively) as acquired by using many of the scanners listed in Table 1 are reported in .