GAIT-BASED HUMAN GENDER CLASSIFICATION USING 5/3 LIFTING BASED WAVELET FILTERS AND PRINCIPAL COMPONENT ANALYSIS
Yükleniyor...
Dosyalar
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SİİRT ÜNİVERSİTESİ FEN BİLİMLERİ ENSTİTÜSÜ
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Researches 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.
Açıklama
Anahtar Kelimeler
Gender classification, Wavelet filters, Decision tree, Gait recognition
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Hassan, 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.
