Face Biometrics for Personal Identification Multi-Sensory Multi-Modal Systems /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Hammoud, Riad I. (Επιμελητής έκδοσης), Abidi, Besma R. (Επιμελητής έκδοσης), Abidi, Mongi A. (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2007.
• Σειρά: Signals and Communication Technology,
• Θέματα: Computer science.; Pattern recognition.; Biometrics (Biology).; Biophysics.; Biological physics.; Computer Science.; Biometrics.; Pattern Recognition.; Signal, Image and Speech Processing.; Biophysics and Biological Physics.

 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. 

 Space/Time Emerging Face Biometrics -- Pose and Illumination Invariant Face Recognition Using Video Sequences -- Recognizing Faces Across Age Progression -- Quality Assessment and Restoration of Face Images in Long Range/High Zoom Video -- Core Faces: A Shift-Invariant Principal Component Analysis (PCA) Correlation Filter Bank for Illumination-Tolerant Face Recognition -- Multi-Sensory Face Biometrics -- Towards Person Authentication by Fusing Visual and Thermal Face Biometrics -- Multispectral Face Recognition: Fusion of Visual Imagery with Physiological Information -- Feature Selection for Improved Face Recognition in Multisensor Images -- Multimodal Face Biometrics -- Multimodal Face and Speaker Identification for Mobile Devices -- Quo Vadis: 3D Face and Ear Recognition? -- Human Recognition at a Distance in Video by Integrating Face Profile and Gait -- Generic Approaches to Multibiometric Systems -- Fusion Techniques in Multibiometric Systems -- Performance Prediction Methodology for Multibiometric Systems.