By David Zhang
Automatic own authentication utilizing biometric details is changing into extra crucial in functions of public defense, entry keep an eye on, forensics, banking, and so forth. Many different types of biometric authentication suggestions were built according to assorted biometric features. although, many of the actual biometric attractiveness recommendations are in line with dimensional (2D) photographs, even though human features are 3 dimensional (3D) surfaces. lately, 3D recommendations were utilized to biometric functions comparable to 3D face, 3D palmprint, 3D fingerprint, and 3D ear attractiveness. This e-book introduces 4 common 3D imaging tools, and offers a few case reviews within the box of 3D biometrics. This ebook additionally contains many effective 3D function 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 basically 3D digicam: 3D hand verification - Multi-view imaging: 3D fingerprint popularity 3D Biometrics: platforms and Applications is a accomplished creation to either theoretical matters and sensible 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 machine technological know-how, electric engineering, structures technology, and data expertise. Researchers and practitioners in and R&D laboratories engaged on safeguard method layout, biometrics, immigration, legislation enforcement, keep an eye on, and development popularity also will locate a lot of curiosity during this book.
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Additional info for 3D Biometrics: Systems and Applications
A couple of seconds suffice to get a 3D representation and compare it to the claimed reference (Beumier and Acheroy 2000). Wang et al. focus on both 3D range data and 2D gray-level facial images. They extracted shape features from 3D feature points which are described by Point Signature in the 3D domain and texture features from 2D feature points which are described by Gabor filter responses in the 2D domain (Wang et al. 2002). Lu et al. focus on the key point feature extraction and select a subset of the facial landmarks as the feature points which include nose tip, two inner eye corners, two outside eye corners, and two mouth corners (Lu and Jain 2005).
Also, the ear has more spatial geometrical information than texture information, but spatial information such as posture, depth, and angle are limited in 2D ear images. In recent years, 3D techniques have been used in biometrics authentication, such as 3D face (Kakadiaris et al. 2007; Samir et al. 2006), 3D palmprint (Zhang 2009) and 3D ear recognition (Chen and Bhanu 2007; Yan and Bowyer 2007; Chen and Bhanu 2009). A 3D ear image is robust to imaging conditions, and contains surface shape information which is related to the anatomical structure as well as being insensitive to environmental illuminations.
4 Posture Normalization Method Using Projection Density There are specific computable projective directions in 3D model, in which points projected to the low dimensional spaces distributed most sparsely. Optimal projection pursuit is utilized to retrieve such directions. Project pursuit can be used to project high dimensional data to low dimensional space and find the optimum projection vector of data in one dimension space by data character of research units. 7. 7 3D ear model of different vertex projection direction Suppose the projective points are P.
3D Biometrics: Systems and Applications by David Zhang