3D Biometrics: Systems and Applications by David Zhang

By David Zhang

Automatic own authentication utilizing biometric info is turning into extra crucial in purposes of public defense, entry regulate, forensics, banking, and so on. Many sorts of biometric authentication ideas were constructed in accordance with diverse biometric features. even though, many of the actual biometric popularity concepts are in line with dimensional (2D) photos, even though human features are 3 dimensional (3D) surfaces. lately, 3D suggestions were utilized to biometric functions similar to 3D face, 3D palmprint, 3D fingerprint, and 3D ear attractiveness. This e-book introduces 4 general 3D imaging tools, and offers a few case reports within the box of 3D biometrics. This publication additionally contains many effective 3D function extraction, matching, and fusion algorithms. those 3D imaging equipment and their functions are given as follows: - unmarried view imaging with line structured-light: 3D ear id - unmarried view imaging with multi-line structured-light: 3D palmprint authentication - unmarried view imaging utilizing simply 3D digicam: 3D hand verification - Multi-view imaging: 3D fingerprint attractiveness 3D Biometrics: platforms and Applications is a entire creation to either theoretical matters and functional implementation in 3D biometric authentication. it's going to function a textbook or as an invaluable reference for graduate scholars and researchers within the fields of desktop technological know-how, electric engineering, structures technology, and knowledge expertise. Researchers and practitioners in and R&D laboratories engaged on protection method layout, biometrics, immigration, legislations enforcement, keep an eye on, and trend popularity also will locate a lot of curiosity during this book.

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235 Turk G, Levoy M (1994) Zippered polygon meshes from range images. In: Proceeding of conference on computer graphics and interactive techniques, pp 311–318. 192241 Yan P, Bowyer KW (2007) Biometric recognition using 3D ear shape. IEEE Trans Pattern Anal Mach Intell 29(8):1297–1308. 1067 Zhang D, Lu GM, Li W, Zhang L, Luo N (2009) Palmprint recognition using 3-D information. IEEE Trans Syst Man Cybern C Appl Rev 39(5):505–519. 2020790 Zhang D (2000) Automated biometrics: technologies and systems.

IEEE Trans Pattern Anal Mach Intell 14:239–256. 121791 Burge M, Burger W (2000) Ear biometrics in computer vision. Proc Int Conf Pattern Recogn 2:822–826. 906202 Chen H, Bhanu B (2007) Human ear recognition in 3D. IEEE Trans Pattern Anal Mach Intell 29(4):718–737. 1005 Chen H, Bhanu B (2009) Efficient recognition of highly similar 3D objects in range images. IEEE Trans Pattern Anal Mach Intell 31(1):172–179. 176 Hurley D, Nixon M, Carter J (2005) Force field feature extraction for ear biometrics.

2002) constructed a system of 16 cameras for realtime rendering. Several large arrays consisting of tens of cameras have also been built, such as the Stanford multi-camera array, the MIT distributed light field camera and the CMU 3D room. These three systems have 128, 64 and 49 cameras, respectively (Wilburn et al. 2002; Yang et al. 2002b; Kanade et al. 1998). In the above camera arrays, those with a small number of cameras can usually achieve real-time rendering. Large camera arrays, despite their increased viewpoint ranges, often have difficulty in achieving satisfactory rendering speed due to the large amount of data to be handled.

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