Multimodal biometrics system based on face profile and ear / Iman Saad Eldeen Youssef Abdulaziz Youssef ; Supervised Ahmed M. Badawi , Mohamed E. Rasmy , Ayman Abaza

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Iman Saad Eldeen Youssef Abdulaziz Youssef

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TextLanguage: English Publication details: Cairo : Iman Saad Eldeen Youssef Abdulaziz Youssef , 2014Description: 64 P. : charts , facsimiles ; 30cmOther title:

نظام القياسات الحيوية المتعدد النماذج على أساس المنظور الجانبي للوجه والأذن [Added title page title]

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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering Summary: Face recognition from side profile view, has recently received significant attention in the literature. This thesis presents an efficient technique for the fusion of face profile and ear biometrics. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods are tested. Active Appearance Models (AAM) are used for object detection (face profile or ear). We propose the use of Block-based Local Binary Pattern (LBP). Experimental results show that the proposed system can achieve about (97.98%) rank-1 identification. Detailed comparisons with other techniques are presented

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Item type	Current library	Home library	Call number	Copy number	Status	Date due	Barcode
Thesis	قاعة الرسائل الجامعية - الدور الاول	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.03.M.Sc.2014.Im.M (Browse shelf(Opens below))		Not for loan		01010110065044000
CD - Rom	مخـــزن الرســائل الجـــامعية - البدروم	المكتبة المركزبة الجديدة - جامعة القاهرة	Cai01.13.03.M.Sc.2014.Im.M (Browse shelf(Opens below))	65044.CD	Not for loan		01020110065044000

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Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

Face recognition from side profile view, has recently received significant attention in the literature. This thesis presents an efficient technique for the fusion of face profile and ear biometrics. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods are tested. Active Appearance Models (AAM) are used for object detection (face profile or ear). We propose the use of Block-based Local Binary Pattern (LBP). Experimental results show that the proposed system can achieve about (97.98%) rank-1 identification. Detailed comparisons with other techniques are presented

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