By David Zhang
Automatic own authentication utilizing biometric details is turning into extra crucial in functions of public safety, entry keep an eye on, forensics, banking, and so forth. Many sorts of biometric authentication concepts were constructed in keeping with assorted biometric features. in spite of the fact that, many of the actual biometric popularity innovations are in accordance with dimensional (2D) photographs, although human features are 3 dimensional (3D) surfaces. lately, 3D innovations were utilized to biometric purposes corresponding to 3D face, 3D palmprint, 3D fingerprint, and 3D ear popularity. This booklet introduces 4 commonplace 3D imaging equipment, and offers a few case experiences within the box of 3D biometrics. This e-book additionally comprises many effective 3D characteristic extraction, matching, and fusion algorithms. those 3D imaging equipment and their purposes are given as follows: - unmarried view imaging with line structured-light: 3D ear identity - unmarried view imaging with multi-line structured-light: 3D palmprint authentication - unmarried view imaging utilizing simply 3D digital camera: 3D hand verification - Multi-view imaging: 3D fingerprint reputation 3D Biometrics: platforms and Applications is a finished creation to either theoretical matters and useful 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 laptop technology, electric engineering, structures technological know-how, and data expertise. Researchers and practitioners in and R&D laboratories engaged on safeguard method layout, biometrics, immigration, legislations enforcement, keep watch over, and trend attractiveness also will locate a lot of curiosity during this book.
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Extra info for 3D Biometrics: Systems and Applications
It detects the 3D surface visibility before coordinate transformation based on the sum of all normal vectors and introduces six different forms of rotation matrix to fix principal direction; however it is still a completely linear method based on statistic, which is sensitive to noise and relies on points’ distribution on surface. We randomly select 2 ear samples from our database. They are collected under different condition for testing. Each of the samples comes from the same person. Due to some external factors, there exist different holes and noises on two samples.
We can see the 3D data cloud is smooth and able to display geometric shapes of the 3D ear. For testing the quality of the 3D ear sample, we computed the Gaussian curvature of the 3D points cloud. 12. It is clear that our 3D data could produce a good quality representation of the 3D ear. 12 Typical 3D ear sample captured by our device Here we compare the proposed method with improved Mesh PCA. Mesh PCA is improved in (Besl and McKay 1992) to solve the problem of direction disorder in the adjustment.
Then the ICP method is performed for alignment refinement to the texture feature map according to the principal line features and shape features when necessary. The alignment refinement reduces greatly the translation and rotation variations introduced in the palmprint data acquisition process. A couple of feature matching rules and an efficient joint 2D and 3D feature matching scheme were then proposed to fully use the 2D and 3D palmprint information. The experimental results on the 2D + 3D palmprint database, which contains 8,000 samples collected from 200 volunteers, demonstrated that the proposed method increases significantly the palmprint verification accuracy.