Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/4018
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dc.contributor.advisorBitar, Amineen_US
dc.contributor.authorDandashy, Tareken_US
dc.date.accessioned2020-12-23T14:39:52Z-
dc.date.available2020-12-23T14:39:52Z-
dc.date.issued2017-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/4018-
dc.descriptionIncludes bibliographical references (p. 61-64).en_US
dc.descriptionSupervised by Dr. Amine Bitar.en_US
dc.description.abstractIn the field of computer vision, face detection is particularly important and in widespread use. Face detection is a specific case of object detection and is used as the primary module in face and gesture recognition systems, face tracking and more. Due to its potential importance, it is a hot topic in computer vision and is under extensive research with many proposed approaches and their variants that have shown increasingly better performance. The main issues that are addressed are detection ability and computational density which limit usability. The most notable advance in this area was introduced in 2001 by the influential work of Viola and Jones and is the basis of the work in this thesis. Many variants of the Viola and Jones framework have been proposed, most notably the variant that uses Multi-Block Local Binary Pattern (MB-LBP) features has shown the best results and is used in real applications. However, strengths and weaknesses are identified in this variant which has prompted for its adoption but with improvement. In this thesis, a comprehensive analysis is performed in a theoretical manner aided by runtime characteristics of the selected variant, to reveal weak points which are then collectively addressed and evaluated. This work will introduce a new feature called Dual-Threshold Haar Feature (DT-HF), which helps extract more information from the image and improves the discrimination ability of the detector, also resulting in much lower computational density. The DT-HFs exhibit best performance when used as a pre-detector designed to reject most non-face image regions quickly before being passed to the MB-LBPs for further analysis. DT-HFs enhance the discrimination power of the detector by significantly decreasing false positives. The work used the BioID and Caltech face detection benchmarking databases to reveal its performance characteristics and for comparison to the other variants. Quantitative evaluation showed favorable results of the proposed face detector with 12% higher detection rate over MB-LBP features, at a given false alarm of 15%. Moreover, the proposed detector processed the BioID face detection database in 11 seconds (128fps), gaining ×3 speedup over using MB-LBP alone. The testing was run on a single core of a 4th gen Intel mobile Core i7 processor.en_US
dc.description.statementofresponsibilityby Tarek Dandashyen_US
dc.format.extentix, 67 p. :ill., tables ;30 cmen_US
dc.language.isoengen_US
dc.rightsThis object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holderen_US
dc.subject.lcshPattern recognition systemsen_US
dc.subject.lcshHuman face recognition (Computer science)en_US
dc.subject.lcshComputer visionen_US
dc.titleEnhanced face detection performance based on multi-block local binary patterns and Dual - Threshold Haar Featuresen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.facultyFaculty of Arts and Sciencesen_US
dc.contributor.institutionUniversity of Balamanden_US
dc.date.catalogued2017-08-31-
dc.description.degreeMSc in Computer Scienceen_US
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=http://olib.balamand.edu.lb/projects_and_theses/GP-CoS-16.pdfen_US
dc.identifier.OlibID173986-
dc.provenance.recordsourceOliben_US
Appears in Collections:UOB Theses and Projects
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