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Automatic image tagging using facial recognition / Mai Mohamed Mahmoud Farag ; Supervised Hesham Ahmed Hefny , Tarek Elghazaly

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mai Mohamed Mahmoud Farag , 2018Description: 112 Leaves : charts , facsimiles ; 30cmOther title:
  • الترميز الآلى للصور باستخدام التعرف الوجهى [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Science Summary: With the popularity of digital cameras, recent years have witnessed a rapid growth of capturing selfie personal images. People capture self-images to record their lives and share them on the web. Face recognition has been one of the most interesting and important approach fields in the past two decades. The reasons come from the need of automatic recognitions systems. Different approaches have been published to overcome different factors (such as illumination, facial expression, scale, pose, {u2026}{u2026}) and achieve better recognition rate. However, there is still room for improvement. In this thesis, face recognition method is being applied to identify human faces using particle swarm optimization (PSO) to optimize Hidden markov model (HMM) states and parameter of face recognition system. Near optimal feature selection for the face images based on the idea of collaborative behavior of bird flocking (PSO) is used to reduce feature size and recognition time. The system examines 400 face images of the olivetti research Laboratory (ORL) face database and 400 face images of the Facebook images database, with a recognition rate of 98.5% and 90% respectively
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.M.Sc.2018.Ma.A (Browse shelf(Opens below)) Not for loan 01010110076012000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.18.02.M.Sc.2018.Ma.A (Browse shelf(Opens below)) 76012.CD Not for loan 01020110076012000

Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Science

With the popularity of digital cameras, recent years have witnessed a rapid growth of capturing selfie personal images. People capture self-images to record their lives and share them on the web. Face recognition has been one of the most interesting and important approach fields in the past two decades. The reasons come from the need of automatic recognitions systems. Different approaches have been published to overcome different factors (such as illumination, facial expression, scale, pose, {u2026}{u2026}) and achieve better recognition rate. However, there is still room for improvement. In this thesis, face recognition method is being applied to identify human faces using particle swarm optimization (PSO) to optimize Hidden markov model (HMM) states and parameter of face recognition system. Near optimal feature selection for the face images based on the idea of collaborative behavior of bird flocking (PSO) is used to reduce feature size and recognition time. The system examines 400 face images of the olivetti research Laboratory (ORL) face database and 400 face images of the Facebook images database, with a recognition rate of 98.5% and 90% respectively

Issued also as CD

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