University of Bahrain
Scientific Journals

Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image

Show simple item record Zulcaffle, Tengku Mohd Afendi Kurugollu, Fatih Kipli, Kuryati Joseph, Annie Bong, David B. L. 2023-07-26T05:52:58Z 2023-07-26T05:52:58Z 2023-09-01
dc.identifier.issn 2210-142X
dc.description.abstract Front and back views gait recognitions are important, especially for narrow corridor applications. Hence, it is important to experiment with new algorithms on the front and back views gait recognitions. In this paper, we present the experiments on gait recognition using the pretrained EfficientNets and EfficientNetV2 models and Gait Energy Image. These models are chosen because they are among the best deep learning models in computer vision. The pretrained models were used in this experiment because it can produce faster and better accuracies compared to training the models from scratch. In addition to the pretrained models, we also propose ensemble models so that they can produce better accuracies. The result shows that the EfficientNetB7-Augm+ EfficientNetB6-Augm is the best overall accuracy (79.59%). However, combining the models slow down the inference speed. So, for recognition speed, EfficientNetB6 and EfficientNetB6-Augm are the best with 87.01ms speed per input image. The results produced are very good considering no cross-view algorithms applied to the Gait Energy Image. Future works will include the cross-view algorithms to further improve the accuracies of the proposed method en_US
dc.language.iso en en_US
dc.publisher University of Bahrain en_US
dc.subject Gait Recognition en_US
dc.subject Deep Learning en_US
dc.subject EfficientNets en_US
dc.subject EfficientNetV2 en_US
dc.title Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image en_US
dc.volume 14 en_US
dc.issue 1 en_US
dc.pagestart 1 en_US
dc.pageend xx en_US
dc.contributor.authorcountry Malaysia en_US
dc.contributor.authorcountry United Arab Emirates en_US
dc.contributor.authoraffiliation Universiti Malaysia Sarawak en_US
dc.contributor.authoraffiliation University of Sharjah en_US
dc.source.title International Journal of Computing and Digital Systems en_US
dc.abbreviatedsourcetitle IJCDS en_US

Files in this item

This item appears in the following Issue(s)

Show simple item record

All Journals

Advanced Search


Administrator Account