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

被引：2

作者：

Thalluri, Lakshmi Narayana ^{[1
]}

Babburu, Kiranmai ^{[2
]}

Madam, Aravind Kumar ^{[3
]}

Kumar, K. V. V. ^{[4
]}

Ganesh, G. V. ^{[5
]}

Rajasekhar, Konari ^{[6
]}

Guha, Koushik ^{[7
]}

Mohammad, Md. Baig ^{[1
]}

Kiran, S. S. ^{[8
]}

Sarma, Addepalli V. S. Y. Narayana ^{[1
]}

Yaswanth, Vegesna Venkatasiva Naga ^{[1
]}

机构：

[1] Andhra Loyola Inst Engn & Technol, Dept Elect & Commun Engn, Dr APJ Abdul Kalam Res Forum, Vijayawada 520008, Andhra Pradesh, India

[2] Baba Inst Technol & Sci, Dept Elect & Commun Engn, Visakhapatnam 530041, AP, India

[3] West Godavari Inst Sci & Engn, Dept Elect & Commun Engn, Tadepalligudem 534112, Andhra Pradesh, India

[4] Vignans Lara Inst Technol & Sci, Dept Elect & Commun Engn, Guntur 522213, AP, India

[5] Koneru Lakshmaiah Educ Fdn, Dept Elect & Commun Engn, Vaddeswaram 522502, AP, India

[6] N S Raju Inst Technol Autonomous, Dept Elect & Commun Engn, Sontyam 531173, AP, India

[7] Natl Inst Technol, Natl MEMS Design Ctr, Dept Elect & Commun Engn, Silchar 788010, Assam, India

[8] Lendi Inst Engn & Technol, Dept Elect & Commun Engn, Vizianagaram 535005, AP, India

来源：

INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS | 2024年 / 8卷 / 01期

基金：

英国科研创新办公室;

关键词：

Deep learning; Convolutional neural networks; Face database; Face recognition; Smart attendance system;

D O I：

10.1007/s41315-023-00310-1

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

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

页码：162 / 178

页数：17

共 29 条

[1] An effective optimization enabled deep learning based Malicious behaviour detection in cloud computing [J].