论文标题
明智的出勤系统USIGN CNN
Smart Attendance System Usign CNN
论文作者
论文摘要
关于出勤系统的研究已经进行了很长时间，在过去的十年中，已经提出了许多安排，以使该系统高效，更耗时，但是所有这些系统都有几个缺陷。在本文中，我们正在使用面部检测和面部识别来引入一个智能有效的系统。该系统可用于在卷积神经网络（CNN）的帮助下使用实时识别的大学或办公室出席。诸如本本特面和费舍尔脸等常规方法对照明，噪音，姿势，阻塞，照明等敏感。因此，我们使用CNN识别面部并克服了此类困难。出勤记录将自动更新，并存储在Excel表以及数据库中。我们已经将MongoDB用作出勤记录的后端数据库。
The research on the attendance system has been going for a very long time, numerous arrangements have been proposed in the last decade to make this system efficient and less time consuming, but all those systems have several flaws. In this paper, we are introducing a smart and efficient system for attendance using face detection and face recognition. This system can be used to take attendance in colleges or offices using real-time face recognition with the help of the Convolution Neural Network(CNN). The conventional methods like Eigenfaces and Fisher faces are sensitive to lighting, noise, posture, obstruction, illumination etc. Hence, we have used CNN to recognize the face and overcome such difficulties. The attendance records will be updated automatically and stored in an excel sheet as well as in a database. We have used MongoDB as a backend database for attendance records.
