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

IF 2.1 Q3 ROBOTICS

International Journal of Intelligent Robotics and Applications Pub Date : 2024-01-09 DOI:10.1007/s41315-023-00310-1

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

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Abstract

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.

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使用卷积神经网络的智能考勤应用人脸自动识别系统

本文利用卷积神经网络（CNN）设计了一种用于智能考勤应用的免触摸自动人脸识别系统。该系统适用于办公室和大学的考勤应用，可有效限制 covid-19 型病毒的传播。CNN 使用专用数据库进行训练，该数据库包含 1890 张具有不同光照度和旋转角度的人脸，共有 30 个目标类别。使用不同的激活函数（即 Step、Sigmoid、Tanh、softmax 和 ReLu）对 9 层和 11 层 CNN 进行了性能分析。采用 ReLu 激活函数的 11 层 CNN 对所设计的人脸数据库的准确率为 96.2%。该系统能够使用 Viola Jones 算法从测试图像中检测出多张人脸。最后，还设计了一个网络应用程序，帮助监测考勤情况并生成报告。

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来源期刊

International Journal of Intelligent Robotics and Applications ROBOTICS-

CiteScore

3.80

自引率

5.90%

发文量

期刊介绍： The International Journal of Intelligent Robotics and Applications (IJIRA) fosters the dissemination of new discoveries and novel technologies that advance developments in robotics and their broad applications. This journal provides a publication and communication platform for all robotics topics, from the theoretical fundamentals and technological advances to various applications including manufacturing, space vehicles, biomedical systems and automobiles, data-storage devices, healthcare systems, home appliances, and intelligent highways. IJIRA welcomes contributions from researchers, professionals and industrial practitioners. It publishes original, high-quality and previously unpublished research papers, brief reports, and critical reviews. Specific areas of interest include, but are not limited to:Advanced actuators and sensorsCollective and social robots Computing, communication and controlDesign, modeling and prototypingHuman and robot interactionMachine learning and intelligenceMobile robots and intelligent autonomous systemsMulti-sensor fusion and perceptionPlanning, navigation and localizationRobot intelligence, learning and linguisticsRobotic vision, recognition and reconstructionBio-mechatronics and roboticsCloud and Swarm roboticsCognitive and neuro roboticsExploration and security roboticsHealthcare, medical and assistive roboticsRobotics for intelligent manufacturingService, social and entertainment roboticsSpace and underwater robotsNovel and emerging applications