Gait-Based Human Gender Classification Using Lifting 5/3 Wavelet and Principal Component Analysis
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Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study describes a representation of gait appearance for the purpose of person identification and classification. The gait representation is based on wavelet 5/3 lifting scheme simple features such as features extracted from video silhouettes of human walking motion. Regardless of its effortlessness, this may lead us to say that, the resulting feature vector contains enough information to perform well on human identification and gender classification tasks. We found out the recognition behaviors of different methods to total features over time functions under different recognition tasks. In addition to that, we provide results of gender classification based on our gait appearance features using a (C4.5 algorithm). So, the result of classification rate for CASIA-B gait databases is 97.98% and the result of recognition rate for OU-ISIR gait Database Large Population Dataset is 97.5%, these results have been obtained from gender classification data. Gait database demonstrates that the proposed method achieves better recognition performance than the most existing methods in the literature, and particularly under certain walking variations. © 2018 IEEE.
Açıklama
2018 International Conference on Advanced Science and Engineering, ICOASE 2018 -- 9 October 2018 through 11 October 2018 -- Duhok, Kurdistan Region -- 143073
Anahtar Kelimeler
C4.5 Algorithm, Gait Recognition, Lifting 5/3, Principal Component Analysis (PCA)
Kaynak
ICOASE 2018 - International Conference on Advanced Science and Engineering
WoS Q Değeri
Scopus Q Değeri
N/A
