GAIT-BASED HUMAN GENDER CLASSIFICATION USING 5/3 LIFTING BASED WAVELET FILTERS AND PRINCIPAL COMPONENT ANALYSIS

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dc.contributor.advisorTİRYAKİ, VOLKAN MÜJDAT
dc.contributor.authorHASSAN, OMER MOHAMMED SALIH HASSAN
dc.date.accessioned2019-11-29T04:31:47Z
dc.date.available2019-11-29T04:31:47Z
dc.date.issued2018en_US
dc.date.submitted2018-06-10
dc.departmentEnstitüler, Fen Bilimleri Enstitüsü, Elektrik-Elektronik Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractResearches about gait recognition systems have begun to spread with the increase of the amount of video data. Human gender can be estimated by using machine learning methods from gait data. In the present study, a human gender classification system is designed by using CASIA - B gait database and OUISIR Gait Database Large Dataset. The silhouettes were extracted from the gait videos, the features were extracted using 5/3 lifting scheme, the feature vectors were then classified using C4.5 decision tree classifier, the genders were obtained, and the system performance was evaluated. Results showed that by using the proposed method, human gender were classified with an accuracy of 97.98% on CASIA - B gait databases and 97.5% recognition rate on OU-ISIR Walk Database large Dataset. This study demonstrates that using gait data followed by proposed feature extraction methods, human gender can be successfully estimated.en_US
dc.description.tableofcontentsTABLE OF CONTENTS Page ACKNOWLEDGEMENT ..........................................................................................................................İİİ TABLE OF CONTENTS ............................................................................................................................İV LIST OF FIGURES ...................................................................................................................................Vİ ABBREVIATIONS AND SYMBOL LISTS ...................................................................................................Vİİ SYMBOL DESCRİPTİON....................................................................................................................Vİİ ÖZET...................................................................................................................................................Vİİİ ABSTRACT.............................................................................................................................................İX 1. INTRODUCTION ................................................................................................................................. 1 1.1 THE AİM OF STUDY................................................................................................................................. 2 1.2 THESİS OUTLİNE..................................................................................................................................... 3 2. LITERATURE REVIEW.......................................................................................................................... 4 3. MATERIALS AND METHODS............................................................................................................... 8 3.1 DATABASE ............................................................................................................................................ 8 3.1.1 CASIA database B ...................................................................................................................... 8 3.1.2 OU-ISIR database....................................................................................................................... 8 3.2.METHOD .......................................................................................................................................... 10 3.2.1 Preprocessing .......................................................................................................................... 10 3.2.2 Outer Contour.......................................................................................................................... 10 3.3. GAİT REPRESENTATİON......................................................................................................................... 11 3.3.1. Continuous Wavelet Transforms ............................................................................................ 11 3.3.2 Discrete Wavelet Transform.................................................................................................... 11 3.3.3 2D Wavelet Transform ............................................................................................................ 13 3.4 WAVELET DATA ACQUİSİTİON AND DATA PRE-PROCESSİNG.......................................................................... 14 3.4.1 Data Acquisition ...................................................................................................................... 14 3.4.2 Wavelet Data Pre-Processing .................................................................................................. 15 3.4.3 Feature Extraction ................................................................................................................... 16 3.4.4 Wavelet Classification ............................................................................................................. 16 3.4.5 Lifting Scheme Based Wavelet Transform............................................................................... 17 3.5 DİMENSİON REDUCTİON USİNG PCA ........................................................................................................ 22 3.6 COEFFİCİENTS DESCRİPTİON.................................................................................................................... 23 C4.5 DECİSİON TREE CLASSİFİCATİON ALGORİTHM............................................................................................. 24 4. RESULTS AND DISCUSSION...................................................................................................... 26 4.1 EXPERIMENTAL IMPLEMENTATION .................................................................................................. 26 4.2 GAİT CYCLE DETECTİON AND FEATURE EXTRACTİON...................................................................................... 29 5. CONCLUSIONS AND FUTURE WORK ..................................................................................... 38 5.1. CONCLUSIONS................................................................................................................................. 38 5.2. FUTURE WORK................................................................................................................................ 39 REFERENCES .................................................................................................................................. 40 CURRICULUM VITAE ............................................................................................................................ 44en_US
dc.identifier.citationHassan, Omer Mohammed Salih Hassan, Gait-based human gender classification using 5/3 lifting based wavelet filters and principal component analysis, Siirt Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi, 2018.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12604/2092
dc.identifier.yoktezid507214
dc.institutionauthorTİRYAKİ, VOLKAN MÜJDAT
dc.language.isoenen_US
dc.publisherSİİRT ÜNİVERSİTESİ FEN BİLİMLERİ ENSTİTÜSÜen_US
dc.relation.publicationcategoryTezen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz#KayıtKontrol#
dc.subjectGender classificationen_US
dc.subjectWavelet filtersen_US
dc.subjectDecision treeen_US
dc.subjectGait recognitionen_US
dc.titleGAIT-BASED HUMAN GENDER CLASSIFICATION USING 5/3 LIFTING BASED WAVELET FILTERS AND PRINCIPAL COMPONENT ANALYSISen_US
dc.typeMaster Thesisen_US

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