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Handbook of multibiometrics
Handbook of Multibiometrics, a professional book, introduces multibiometric systems, and demonstrates the noteworthy advantages of these systems over their unimodal counterparts. In addition, this book describes in detail the various scenarios that are possible when fusing biometric evidence from multiple information sources. This comprehensive volume on multibiometric systems concisely and clearly outlines the different fusion methodologies that have been proposed by researchers to integrate multiple biometric traits.
Face Biometrics for Personal Identification : Multi-Sensory Multi-Modal Systems
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. 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.
