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 anyone as such, has more and more been said as an important point of varied defense purposes. a number of reputation and identity platforms were in line with biometrics using biometric beneficial properties resembling fingerprint, face, retina scans, iris styles, hand geometry, DNA strains, gait, and others.
This booklet originates from a world summer time university on biometrics, held in Alghero, Italy, in June 2003. The seven revised instructional lectures via prime researchers introduce the reader to biometrics-based individual authentication, fingerprint reputation, gait reputation, a variety of facets of face popularity and face detection, topologies for biometric popularity, and hand detection. additionally incorporated are the 4 most sensible chosen pupil papers, all facing face recognition.
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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
Alternatively, one may only be interested in the outline of that region. We refer to this outline as a silhouette. An examples of gait analysis that uses silhouettes is in Baumberg and Hogg . 3 Optical Flow A motion ﬁeld, is a projection of motion in a scene onto the image plane. Optical ﬂow refers to the movement or ﬂow of pixel brightness in an image sequence, and is a quantity that we can estimate from images sequences. Although the motion ﬁeld and optical ﬂow are not the same, we often use optical ﬂow as an approximation to the motion ﬁeld since most ﬂow is caused by observed motion.
In the following subsections, we categorize the methods by their source of oscillations: shape, joint trajectory, self similarity, and pixel. 1 Shape Oscillations Fig. 9 shows the shape-of-motion system developed by Little and Boyd . The system uses optical ﬂow to identify a moving ﬁgure in a sequence of images. It then describes the shape of the moving ﬁgure with a set of scalars derived from Cartesian moments. 2 Random 0 1 10 Sample Size 100 Fig. 8. Performance comparison of biometric gait recognition systems showing recognition rate versus sample size.
If a sample produces a value of f that is large enough, one rejects the null hypothesis and concludes that there is signiﬁcant variation between classes. Note that sample size is a parameter of the known distributions of f , so f may be interpreted for samples of diﬀerent size. Bobick and Johnson  describe √ expected confusion, E[A], a number that is directly related to f (E[A] = 1/ f ), and its role in predicting performance for varying sample size. While f address issues of sample size, it is not clear how to compare f for diﬀerent feature spaces, especially when data can be linear, as in a persons height, or directional, as in the phase of a signal.
Advanced Studies in Biometrics: Summer School on Biometrics, Alghero, Italy, June 2-6, 2003. Revised Selected Lectures and Papers by J. Bigun, J. Fierrez-Aguilar, J. Ortega-Garcia, J. Gonzalez-Rodriguez (auth.), Massimo Tistarelli, Josef Bigun, Enrico Grosso (eds.)