Etkili Cinsiyet Tanıma için Yürüyüş Verisinin Kullanımı

Year 2021, Issue: 32, 27 - 31, 31.12.2021

Zehra Karapınar Şentürk

https://doi.org/10.31590/ejosat.1040002

Abstract

Biyometrik tanıma uygulamaları, çoğunlukla otomatik algılama için güvenilirlik ve kullanım kolaylığı nedeniyle günümüzde sıklıkla kullanılmaktadır. Kimlik doğrulama ve cinsiyet sınıflandırması için göz, yüz, parmak izi ve sese dayalı birçok uygulama bulunmaktadır. Bu makalede, insanların adımlarının özelliklerini kullanarak cinsiyet tespiti üzerinde durduk. Farklı bir biyometrik işaret araştırıldı. Kişilerin cinsiyet bilgilerini belirlemek için yürüyüş analizleri yapıldı. Bir yürüyüşün hızı, değişkenliği, simetrisi gibi temel parametreleri, Physilog 5 sensörü ile elde edilen çeşitli geçici, uzamsal ve yükseklik parametreleri analizde kullanılmıştır. Bu öznitelikler temel alınarak bir 321-D öznitelik vektörü oluşturulmuş ve bunlarla bir Yapay Sinir Ağları (YSA) modeli eğitilmiştir. %95.83 doğruluk elde edildi. Deneysel sonuçlar, önerilen YSA tabanlı yürüyüş analizi sisteminin cinsiyet sınıflandırması için en son teknolojiye karşı başarısını göstermektedir.

Keywords

Yapay Sinir Ağları, Yürüyüş Analizi, Cinsiyet Sınıflandırması.

Gait Data for Efficient Gender Recognition

Abstract

Biometric recognition applications have been frequently used nowadays mostly because of reliability and ease of use for automated detection. There are many applications based on eyes, face, fingerprint, and voice for authentication and gender classification. In this paper, we focused on gender detection using the features of the steps of people. A different biometric sign has been investigated. Gait analyses were examined to determine the gender information of the people. Basic parameters like speed, variability, and symmetry of a gait, its several temporary, spatial, and height parameters, which were obtained via Physilog 5 sensor, were used in the analysis. A 321-D feature vector was comprised based on these features and an Artificial Neural Networks (ANN) model was trained with them. 95.83% accuracy was obtained. The experimental results show the success of the proposed ANN-based gait analysis system against the state-of-the-art for gender classification.

Keywords

Artificial Neural Networks, Gait Analysis, Gender Classification

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