By David Zhang
Automatic own authentication utilizing biometric details is changing into extra crucial in purposes of public safeguard, entry regulate, forensics, banking, and so on. Many forms of biometric authentication options were built in line with various biometric features. even if, many of the actual biometric acceptance strategies are according to dimensional (2D) photos, even though human features are 3 dimensional (3D) surfaces. lately, 3D suggestions were utilized to biometric functions akin to 3D face, 3D palmprint, 3D fingerprint, and 3D ear acceptance. This booklet introduces 4 ordinary 3D imaging equipment, and offers a few case reviews within the box of 3D biometrics. This booklet additionally comprises many effective 3D characteristic extraction, matching, and fusion algorithms. those 3D imaging tools and their purposes 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 purely 3D digicam: 3D hand verification - Multi-view imaging: 3D fingerprint reputation 3D Biometrics: platforms and Applications is a complete creation to either theoretical concerns and functional implementation in 3D biometric authentication. it is going to function a textbook or as an invaluable reference for graduate scholars and researchers within the fields of computing device technological know-how, electric engineering, platforms technology, and data expertise. Researchers and practitioners in and R&D laboratories engaged on safeguard procedure layout, biometrics, immigration, legislation enforcement, regulate, and trend popularity also will locate a lot of curiosity during this book.
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Additional info for 3D Biometrics: Systems and Applications
The sectional curvatures can be used for human gender classification and an accuracy of 81 % is obtained in our database. 5 % is realized by including Level Zero Features into fingerprint recognition which demonstrates the effectiveness of 3D fingerprint recognition. Simple feature extraction and matching algorithm are used in the system. We believe that higher accuracy can be achieved if more advanced feature extraction and matching methods are proposed in the future. Discover the relationship between different levels of fingerprint features and propose more powerful fusion strategy will future improve 3D fingerprint recognition performance.
The computer controls a projector to project a series of laser-light stripes on the ear surface, and the CCD camera captures the ear images with projected stripes on it. At the same time the computer sends a command to the data collection board to store the images. The data collection takes about 2 s. From these ear images, the depth information of each point on the ear can be computed using lasertriangulation techniques. 2, will take around 1 s. Therefore, the total time for 3D ear generation is about 3 s.
In: Proceedings of the workshop multimodal user authentication, pp 91–98 Chen H, Bhanu B (2005) Contour matching for 3D ear recognition. In: Proceedings of the seventh IEEE workshop on applications of computer vision. 38 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 Chen Y, Parziale G, Diaz-Santana E, Jain AK (2006) 3D touchless fingerprints: compatibility with legacy rolled images.