Automated face recognition system for smart attendance application using convolutional neural networks

被引：0
作者：
Lakshmi Narayana Thalluri
Kiranmai Babburu
Aravind Kumar Madam
K. V. V. Kumar
G. V. Ganesh
Konari Rajasekhar
Koushik Guha
Md. Baig Mohammad
S. S. Kiran
Addepalli V. S. Y. Narayana Sarma
Vegesna Venkatasiva Naga Yaswanth
机构：
[1] Andhra Loyola Institute of Engineering and Technology,Dr. A. P. J. Abdul Kalam Research Forum, Department of Electronics and Communication Engineering
[2] Baba Institute of Technology and Sciences,Department of Electronics and Communication Engineering
[3] West Godavari Institute of Science and Engineering,Department of Electronics and Communication Engineering
[4] Vignans Lara Institute of Technology and Science,Department of Electronics and Communication Engineering
[5] Koneru Lakshmaiah Education Foundation,Department of Electronics and Communication Engineering
[6] N S Raju Institute of Technology (Autonomous),Department of Electronics and Communication Engineering
[7] National MEMS Design Center,Department of Electronics and Communication Engineering
[8] National Institute of Technology,Department of Electronics and Communication Engineering
[9] Lendi Institute of Engineering and Technology,undefined
来源：
International Journal of Intelligent Robotics and Applications | 2024年 / 8卷
关键词：
Deep learning; Convolutional neural networks; Face database; Face recognition; Smart attendance system;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a touch less automated face recognition system for smart attendance application was designed using convolutional neural network (CNN). The presented touch less smart attendance system is useful for offices and college’s attendance applications with this the spread of covid-19 type viruses can be restrict. The CNN was trained with dedicated database of 1890 faces with different illumination levels and rotate angles of total 30 targeted classes. A CNN performance analysis was done with 9-layer and 11-layer with different activation functions i.e., Step, Sigmoid, Tanh, softmax, and ReLu. An 11-layer CNN with ReLu activation function offers an accuracy of 96.2% for the designed face database. The system is capable to detect multiple faces from test images using Viola Jones algorithm. Eventually, a web application was designed which helps to monitor the attendance and to generate the report.
引用
收藏
页码：162 / 178
页数：16
相关论文
共 82 条
[1]  
Bhingarkar S(2022)An effective optimization enabled deep learning based Malicious behaviour detection in cloud computing Int. J. Intell. Robot. Appl. 18 1351-1368
[2]  
Revathi ST(2023)Smart City IoT system network level routing analysis and blockchain security based implementation J. Electr. Eng. Technol. 7 59069-59080
[3]  
Kolli CS(2006)Face recognition using IPCA-ICA algorithm IEEE Trans. Patt. Analy. Mach. Intell. 1 243-254
[4]  
Bommu S(2019)Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks IEEE Access 20 342-206
[5]  
Babburu K(2017)Robotic sensing and object recognition from thermal-mapped point clouds Int J Intell Robot Appl 6 191-1857
[6]  
Srikanth N(2020)Face recognition systems: a survey Sensors 24 1837-393
[7]  
Thalluri LN(2022)"Deep learning, reinforcement learning, and world models Neural Netw. 152 380-380
[8]  
Gopalan A(2022)Bayesian cue integration of structure from motion and CNN-based monocular depth estimation for autonomous robot navigation Int. J. Intell. Robot. Appl. 2 372-1440
[9]  
Mallapati PK(2022)Target application based design approach for RF MEMS switches using artificial neural networks Trans. Electr. Electron. Mater. 21 1427-4
[10]  
Guha K(2020)AI in medical imaging informatics: current challenges and future directions IEEE J. Biomed. Health Inform. 3 1-undefined
123456789