1. bookVolume 26 (2021): Edizione 2 (December 2021)
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2255-8691
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08 Nov 2012
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Real-Time Identification from Gait Features Using Cascade Voting Method

Pubblicato online: 30 Dec 2021
Volume & Edizione: Volume 26 (2021) - Edizione 2 (December 2021)
Pagine: 164 - 172
Dettagli della rivista
License
Formato
Rivista
eISSN
2255-8691
Prima pubblicazione
08 Nov 2012
Frequenza di pubblicazione
2 volte all'anno
Lingue
Inglese
Abstract

There are several biometric methods for identification. These are generally classified under two main groups as physiological and behavioural biometric methods. Recently, methods using behavioural biometric features have gained popularity. Identification made using gait pattern is also one of these methods. The present study proposes a machine learning based system performing identification in real time via gait features using a Kinect device. The data set is composed of 23 individuals’ skeleton model data obtained by the authors. From these data, 147 handcrafted features have been extracted. Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting (GB), XG-Boost (XGB) and K-Nearest Neighbour (KNN) classifiers have been trained with these features. Furthermore, the output of these five machine learning models has been combined with a voting approach. The highest classification has been obtained with 97.5 % accuracy via a voting approach. The classification accuracies of the RF, DNN, XGB, GB and KNN classifiers are 95 %, 87.5 %, 85 %, 80 % and 65 %, respectively. The classification accuracy obtained via a voting approach is higher than in the previous studies. The developed system successfully performs real-time identification.

Keywords

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