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Bhanu B., Chen H. Human Ear Recognition by Computer

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Bhanu B., Chen H. Human Ear Recognition by Computer
Издательство Springer, 2008, -212 pp.
Biometrics deals with recognition of individuals based on their physiological or behavioral characteristics. Researchers have done extensive biometric studies of fingerprints, faces, palm prints, irises and gaits. The Ear is a viable new class of biometrics with certain advantages over faces and fingerprints, which are the two most common biometrics in both academic research and industrial applications. For example, the ear is rich in features; it is a stable structure that does not change much with age and it does not change its shape with facial expressions. Furthermore, the ear is larger in size compared to fingerprints but smaller as compared to the face; and it can be easily captured from a distance without a fully cooperative subject, although sometimes it can be hidden with hair, or with a turban, muffler, scarf or earrings. The ear is made up of standard features like the face. These include the outer rim (helix) and ridges (anti-helix) parallel to the rim, the lobe, the concha (hollow part of ear) and the tragus (the small prominence of cartilage over the meatus).
Researchers have developed several biometrics techniques using 2D intensity images. The performance of these techniques is greatly affected by the pose variation and imaging conditions. However, an ear can be imaged in 3D using a range sensor that provides a registered color and range image pair. An example is shown on the cover of this book. A range image is relatively insensitive to illumination and contains surface shape information related to the anatomical structure, which makes it possible to develop robust 3D ear biometrics. This book explores all aspects of 3D ear recognition: representation, detection, recognition, indexing and performance prediction. The experimental results on the UCR (University of California at Riverside) dataset of 155 subjects with 902 images under pose variations and the University of Notre Dame dataset of 326 subjects with 700 images with time-lapse gallery-probe pairs are presented to compare and demonstrate the effectiveness of proposed algorithms and systems.
The book describes complete human recognition systems using 3D ear biometrics. It also presents various algorithms that will be of significant interest to students, engineers, scientists and application developers involved in basic and applied research in the areas of biometrics and 3D object recognition.
Introduction
Ear Detection and Recognition in 2D and 3D
3D Ear Detection from Side Face Range Images
Recognizing 3D Ears Using Ear Helix/Anti-Helix
Recognizing 3D Ears Using Local Surface Patches
Rapid 3D Ear Indexing and Recognition
Performance Prediction of a 3D Ear Recognition System
Applications of the Proposed Techniques
Summary and FutureWork
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