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Face Biometrics for Personal Identification: Multi-Sensory Multi-Modal Systems (Signals and Communication Technology)
 
 
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Face Biometrics for Personal Identification: Multi-Sensory Multi-Modal Systems (Signals and Communication Technology) [Hardcover]

Riad Hammoud (Editor), Besma Abidi (Editor), Mongi A. Abidi (Editor)


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Book Description

March 12, 2007 3540493441 978-3540493440 1

This book provides an ample coverage of theoretical and experimental state-of-the-art work as well as new trends and directions in the biometrics field. It offers students and software engineers a thorough understanding of how some core low-level building blocks of a multi-biometric system are implemented. While this book covers a range of biometric traits including facial geometry, 3D ear form, fingerprints, vein structure, voice, and gait, its main emphasis is placed on multi-sensory and multi-modal face biometrics algorithms and systems. "Multi-sensory" refers to combining data from two or more biometric sensors, such as synchronized reflectance-based and temperature-based face images. "Multi-modal" biometrics means fusing two or more biometric modalities, like face images and voice timber. This practical reference contains four distinctive parts and a brief introduction chapter. The first part addresses new and emerging face biometrics. Emphasis is placed on biometric systems where single sensor and single modality are employed in challenging imaging conditions. The second part on multi-sensory face biometrics deals with the personal identification task in challenging variable illuminations and outdoor operating scenarios by employing visible and thermal sensors. The third part of the book focuses on multi-modal face biometrics by integrating voice, ear, and gait modalities with facial data. The last part presents generic chapters on multi-biometrics fusion methodologies and performance prediction techniques.


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From the Back Cover

This book provides an ample coverage of theoretical and experimental state-of-the-art work as well as new trends and directions in the biometrics field. It offers students and software engineers a thorough understanding of how some core low-level building blocks of a multi-biometric system are implemented. While this book covers a range of biometric traits including facial geometry, 3D ear form, fingerprints, vein structure, voice, and gait, its main emphasis is placed on multi-sensory and multi-modal face biometrics algorithms and systems. "Multi-sensory" refers to combining data from two or more biometric sensors, such as synchronized reflectance-based and temperature-based face images. "Multi-modal" biometrics means fusing two or more biometric modalities, like face images and voice timber. This practical reference contains four distinctive parts and a brief introduction chapter. The first part addresses new and emerging face biometrics. Emphasis is placed on biometric systems where single sensor and single modality are employed in challenging imaging conditions. The second part on multi-sensory face biometrics deals with the personal identification task in challenging variable illuminations and outdoor operating scenarios by employing visible and thermal sensors. The third part of the book focuses on multi-modal face biometrics by integrating voice, ear, and gait modalities with facial data. The last part presents generic chapters on multi-biometrics fusion methodologies and performance prediction techniques.

Product Details

  • Hardcover: 276 pages
  • Publisher: Springer; 1 edition (March 12, 2007)
  • Language: English
  • ISBN-10: 3540493441
  • ISBN-13: 978-3540493440
  • Product Dimensions: 9.4 x 6.6 x 0.6 inches
  • Shipping Weight: 1.2 pounds
  • Amazon Best Sellers Rank: #3,659,289 in Books (See Top 100 in Books)

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Inside This Book (learn more)
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
jth matcher, face profile images, craniofacial growth model, separated face images, gait classifier, strain transformation model, magnification blur, multibiometric systems, congruency features, infrared face recognition, biometric matchers, data level fusion, joint illumination, profile classifier, sharpness measures, advanced correlation filters, different matchers, missing feature theory, biometric sources, illumination coefficients, ear recognition, face biometrics, individual matchers, speaker identification system, deblurring algorithms
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Computer Science, Electrical Engineering, Carnegie Mellon, University of Houston, Origin Origin, University of Maryland, Assistant Professor, Associate Professor, Computer Society, University of California, Washington University, College of Engineering, Massachusetts Institute of Technology, University of Tennessee, Computational Biomedicine Lab, Old Dominion University, Rank Fig, Thermal Fig, University of Oxford
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