Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image
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...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
University of Bahrain
2023
|
Subjects: | |
Online Access: | http://ir.unimas.my/id/eprint/44802/1/Front%20and%20Back%20Views%20Gait%20-%20Copy.pdf http://ir.unimas.my/id/eprint/44802/ https://journal.uob.edu.bh/handle/123456789/5178 http://dx.doi.org/10.12785/ijcds/140157 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.unimas.ir.44802 |
---|---|
record_format |
eprints |
spelling |
my.unimas.ir.448022024-05-20T07:10:04Z http://ir.unimas.my/id/eprint/44802/ Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image Tengku Mohd Afendi, Zulcaffle Fatih, Kurugollu Kuryati, Kipli Annie, Joseph David Bong, Boon Liang TK Electrical engineering. Electronics Nuclear engineering 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. University of Bahrain 2023 Article PeerReviewed text en http://ir.unimas.my/id/eprint/44802/1/Front%20and%20Back%20Views%20Gait%20-%20Copy.pdf Tengku Mohd Afendi, Zulcaffle and Fatih, Kurugollu and Kuryati, Kipli and Annie, Joseph and David Bong, Boon Liang (2023) Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image. International Journal of Computing and Digital Systems, 14 (1). pp. 749-758. ISSN 2210-142X https://journal.uob.edu.bh/handle/123456789/5178 http://dx.doi.org/10.12785/ijcds/140157 |
institution |
Universiti Malaysia Sarawak |
building |
Centre for Academic Information Services (CAIS) |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaysia Sarawak |
content_source |
UNIMAS Institutional Repository |
url_provider |
http://ir.unimas.my/ |
language |
English |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Tengku Mohd Afendi, Zulcaffle Fatih, Kurugollu Kuryati, Kipli Annie, Joseph David Bong, Boon Liang Front and Back Views Gait Recognitions Using EfficientNets and EfficientNetV2 Models Based on Gait Energy Image |
description |
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. |
format |
Article |
author |
Tengku Mohd Afendi, Zulcaffle Fatih, Kurugollu Kuryati, Kipli Annie, Joseph David Bong, Boon Liang |
author_facet |
Tengku Mohd Afendi, Zulcaffle Fatih, Kurugollu Kuryati, Kipli Annie, Joseph David Bong, Boon Liang |
author_sort |
Tengku Mohd Afendi, Zulcaffle |
title |
Front and Back Views Gait Recognitions Using EfficientNets
and EfficientNetV2 Models Based on Gait Energy Image |
title_short |
Front and Back Views Gait Recognitions Using EfficientNets
and EfficientNetV2 Models Based on Gait Energy Image |
title_full |
Front and Back Views Gait Recognitions Using EfficientNets
and EfficientNetV2 Models Based on Gait Energy Image |
title_fullStr |
Front and Back Views Gait Recognitions Using EfficientNets
and EfficientNetV2 Models Based on Gait Energy Image |
title_full_unstemmed |
Front and Back Views Gait Recognitions Using EfficientNets
and EfficientNetV2 Models Based on Gait Energy Image |
title_sort |
front and back views gait recognitions using efficientnets
and efficientnetv2 models based on gait energy image |
publisher |
University of Bahrain |
publishDate |
2023 |
url |
http://ir.unimas.my/id/eprint/44802/1/Front%20and%20Back%20Views%20Gait%20-%20Copy.pdf http://ir.unimas.my/id/eprint/44802/ https://journal.uob.edu.bh/handle/123456789/5178 http://dx.doi.org/10.12785/ijcds/140157 |
_version_ |
1800728143115321344 |
score |
13.18916 |