Smart office automation via faster R-CNN based face recognition and internet of things
Many constraints limit the accuracy level of classification of a face recognition system in smart office automation application, and these limitations make mask face recognition an important research area. In this research, a novel deep learning based Faster R-CNN which integrates with Internet of T...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423000557 |
| _version_ | 1797797105252696064 |
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| author | G. Rajeshkumar M. Braveen R. Venkatesh P. Josephin Shermila B. Ganesh Prabu B. Veerasamy B. Bharathi A. Jeyam |
| author_facet | G. Rajeshkumar M. Braveen R. Venkatesh P. Josephin Shermila B. Ganesh Prabu B. Veerasamy B. Bharathi A. Jeyam |
| author_sort | G. Rajeshkumar |
| collection | DOAJ |
| description | Many constraints limit the accuracy level of classification of a face recognition system in smart office automation application, and these limitations make mask face recognition an important research area. In this research, a novel deep learning based Faster R-CNN which integrates with Internet of Things (IoT) to overcome the security issues in the office. The images of existing employees were gathered in a database and these images are pre-processed to train the neural network. Faster R-CNN employs VGG-16 as the foundation of its architecture to extract the features from pre-processed pictures. The recent development in Internet of Things (IoT) and deep learning have made it possible to addressing the difficulties of face recognition with deep neural network. Based on the feature classification, when a member of an organization approaches the door, it instantly opens. The door remains locked if it is an unknown individual. The images of a both authorized and unauthorized person were stored in a cloud and send it to the office manager for monitoring. The proposed Faster R-CNN model attain the accuracy range 99.3% better than the existing system. The proposed Faster R-CNN improves the overall accuracy ranges of 2.06%, 5.63%, 9.36%, and 3.54% better than Deep CNN, SVM, LBPH, and OMTCNN respectively. |
| first_indexed | 2024-03-13T03:43:09Z |
| format | Article |
| id | doaj.art-0176bd8c08024a19b33adba0ad5d06c6 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-13T03:43:09Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-0176bd8c08024a19b33adba0ad5d06c62023-06-23T04:44:08ZengElsevierMeasurement: Sensors2665-91742023-06-0127100719Smart office automation via faster R-CNN based face recognition and internet of thingsG. Rajeshkumar0M. Braveen1R. Venkatesh2P. Josephin Shermila3B. Ganesh Prabu4B. Veerasamy5B. Bharathi6A. Jeyam7Department of Information Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, 638 401, Tamil Nadu, India; Corresponding author.School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, Tamil Nadu, IndiaDepartment of Information Technology, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, IndiaDepartment of Artificial Intelligence and Data Science, R.M.K. College of Engineering and Technology, Chennai, Tamil Nadu, IndiaDepartment of Electrical and Electronics Engineering, University College of Engineering Dindigul, Tamilnadu, IndiaDepartment of ECE, Kalasalingam Academy of Research and Education, Krishnankoil, 626126, Tamilnadu, IndiaDepartment of Information Technology, Coimbatore Institute of Engineering and Technology (CIET), Coimbatore, 641109, Tamilnadu, IndiaComputer Science and Engineering, Lord Jegannath College of Engineering and Technology, Tamilnadu, 629402, IndiaMany constraints limit the accuracy level of classification of a face recognition system in smart office automation application, and these limitations make mask face recognition an important research area. In this research, a novel deep learning based Faster R-CNN which integrates with Internet of Things (IoT) to overcome the security issues in the office. The images of existing employees were gathered in a database and these images are pre-processed to train the neural network. Faster R-CNN employs VGG-16 as the foundation of its architecture to extract the features from pre-processed pictures. The recent development in Internet of Things (IoT) and deep learning have made it possible to addressing the difficulties of face recognition with deep neural network. Based on the feature classification, when a member of an organization approaches the door, it instantly opens. The door remains locked if it is an unknown individual. The images of a both authorized and unauthorized person were stored in a cloud and send it to the office manager for monitoring. The proposed Faster R-CNN model attain the accuracy range 99.3% better than the existing system. The proposed Faster R-CNN improves the overall accuracy ranges of 2.06%, 5.63%, 9.36%, and 3.54% better than Deep CNN, SVM, LBPH, and OMTCNN respectively.http://www.sciencedirect.com/science/article/pii/S2665917423000557Smart office automationFace recognitionFaster R-CNNInternet of things |
| spellingShingle | G. Rajeshkumar M. Braveen R. Venkatesh P. Josephin Shermila B. Ganesh Prabu B. Veerasamy B. Bharathi A. Jeyam Smart office automation via faster R-CNN based face recognition and internet of things Measurement: Sensors Smart office automation Face recognition Faster R-CNN Internet of things |
| title | Smart office automation via faster R-CNN based face recognition and internet of things |
| title_full | Smart office automation via faster R-CNN based face recognition and internet of things |
| title_fullStr | Smart office automation via faster R-CNN based face recognition and internet of things |
| title_full_unstemmed | Smart office automation via faster R-CNN based face recognition and internet of things |
| title_short | Smart office automation via faster R-CNN based face recognition and internet of things |
| title_sort | smart office automation via faster r cnn based face recognition and internet of things |
| topic | Smart office automation Face recognition Faster R-CNN Internet of things |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423000557 |
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