By Svetlana N. Yanushkevich, Adrian Stoica, Vlad P. Shmerko, Denis V. Popel
Conventional equipment of biometric research are not able to beat the restrictions of latest techniques, commonly as a result loss of criteria for enter info, privateness issues concerning use and garage of tangible biometric information, and unacceptable accuracy. Exploring suggestions to inverse difficulties in biometrics transcends such limits and permits wealthy research of biometric info and platforms for superior functionality and checking out. even though a few specific inverse difficulties look within the literature, beforehand there was no accomplished reference for those difficulties. Biometric Inverse difficulties offers the 1st accomplished therapy of biometric information synthesis and modeling. This groundbreaking reference contains 8 self-contained chapters that disguise the rules of biometric inverse difficulties; fundamentals of knowledge constitution layout; new automated artificial signature, fingerprint, and iris layout; artificial faces and DNA; and new instruments for biometrics in line with Voronoi diagrams. in accordance with the authors' giant event within the box, the publication authoritatively examines new ways and methodologies in either direct and inverse biometrics, supplying valuable analytical and benchmarking instruments. The authors contain case experiences, examples, and implementation codes for sensible representation of the tools. Loaded with nearly two hundred figures, 60 difficulties, 50 MATLAB® code fragments, and 2 hundred examples, Biometric Inverse difficulties units the traditional for innovation and authority in biometric facts synthesis, modeling, and research.
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Additional info for Biometric Inverse Problems
Most biometric devices can be designed using the same tools. Reason 2. Information fusion can be implemented in various levels of design and various biometrics. Reason 3. Reliability of biometric devices and systems can be improved by combining several biometrics. Reason 4. Application of biometric devices can be extended, for example, to robotics, reconstructive surgery, intelligent decision support, and lie detectors. ) biometric information. 25 Characteristics of voice are pitch, tone, and frequency.
32 Biometric Inverse Problems (c) Expert systems. There are several levels of application of artiﬁcial intelligence tools, in particular: Local optimization problems, Global optimization problems, and Joint human-machine decision making. 27 In applying a library of primitives to the design of synthetic biometric information, the key idea is to adapt computing technology that enables us to navigate, explore, and discover a set of primitives representing a concept. The essence of learning through the concept of biometric primitives lies on the formalism that information coded in primitives can be explicitly represented as (a) A mathematical component that could be simply sequences such as a geometric series, splines, systems of diﬀerential equations, or complex systems for the description of fuzzy data.
6). Direct Fourier transform: Time domain Direct Fourier transform Frequency domain X[k] x[n] N −1 k=0 Inverse Fourier transform: X[k] = Frequency domain X[k] Inverse Fourier transform Time domain x[n] X[k]ejkΩ0 n x[n] = 1 N N −1 x[n]e−jkΩ0 n , n=0 x[n] is a periodic signal with fundamental period N and fundamental frequency Ω0 = 2π/N , X[k] are the coeﬃcients of the signal x[n]. 6 Direct and inverse Fourier transforms. A system is a set of related entities, components or elements. A system is invertible if the input to the system can be recovered from the output 12 Biometric Inverse Problems except for a constant scale factor.