<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 <italic>K</italic>-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.</p>
</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.

Real-Time Identification from Gait Features Using Cascade Voting Method

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Real-Time Identification from Gait Features Using Cascade Voting Method"}

There are several biometric methods for identification. These are generally classified under two main groups as physiological and behavioural biometric...

Real-Time Identification from Gait Features Using Cascade Voting Method

Data publikacji: 30 gru 2021

Zakres stron: 164 - 172

DOI: https://doi.org/10.2478/acss-2021-0020

Słowa kluczowe
Deep neural network, identification, Kinect, machine learning, voting approach

© 2021 Berk Ercin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Real-Time Identification from Gait Features Using Cascade Voting Method

Data publikacji: 30 gru 2021

Zakres stron: 164 - 172

DOI: https://doi.org/10.2478/acss-2021-0020

Słowa kluczoweDeep neural network, identification, Kinect, machine learning, voting approach

© 2021 Berk Ercin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Deep neural network, identification, Kinect, machine learning, voting approach