Cloud-based architecture for face identification with deep learning using convolutional neural network
The use of a face as a biometric to identify a person in order to keep the system safe from an unauthorized person has advantages over other biometric characteristics. The face as a biometric has more structure and a wider area than other biometrics, while can be retrieved in a non-invasive manner....
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-08-01.
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| Online Access: | Get fulltext |
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| LEADER | 02718 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs24375_15347 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Herlambang, Aditya |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Buana, Putu Wira |e author |
| 700 | 1 | 0 | |a Piarsa, I Nyoman |e author |
| 245 | 0 | 0 | |a Cloud-based architecture for face identification with deep learning using convolutional neural network |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-08-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24375 | ||
| 520 | |a The use of a face as a biometric to identify a person in order to keep the system safe from an unauthorized person has advantages over other biometric characteristics. The face as a biometric has more structure and a wider area than other biometrics, while can be retrieved in a non-invasive manner. We proposed a cloud-based architecture for face identification with deep learning using convolutional neural network. Face identification in this study used a cloud-based engine with four stages, namely face detection with histogram of oriented gradients (HOG), image enhancement, feature extraction using convolutional neural network, and classification using k-nearest neighbor (KNN), SVM, as well as random forest algorithm. This study conducted a classification experiment with cloud-based architecture using three different datasets, namely Faces94, Faces96 and University of Manchester Institute of Science and Technology (UMIST) face dataset. The results from this study are with the proposed cloud-based architecture, the best accuracy is obtained by KNN algorithm with an accuracy of 99% on Faces94 dataset, 99% accuracy on Faces96 dataset, 97% on UMIST face dataset, and performance of the three algorithms decreased in UMIST face dataset with facial variations from various angles from left to right profile. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Signal processing; Image processing; Machine learning. | ||
| 690 | |a Deep learning; Face identification; K-nearest neighbor; Random forest; Support vector machine | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 23, No 2: August 2021; 811-820 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v23.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24375/15347 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24375/15347 |z Get fulltext |