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.

在本文中，使用卷积神经网络（CNN）设计了一种用于智能考勤应用的非接触式自动人脸识别系统。所提出的非接触式智能考勤系统对于办公室和大学的考勤应用非常有用，可以限制 covid-19 型病毒的传播。CNN 使用包含 1890 张面孔的专用数据库进行训练，这些面孔具有不同的照明水平和总共 30 个目标类别的旋转角度。使用不同的激活函数（即 Step、Sigmoid、Tanh、softmax 和 ReLu）对 9 层和 11 层进行了 CNN 性能分析。具有 ReLu 激活函数的 11 层 CNN 为设计的人脸数据库提供了 96.2% 的准确率。该系统能够使用 Viola Jones 算法从测试图像中检测多个人脸。最终，设计了一个网络应用程序来帮助监控出勤情况并生成报告。