Monitoring Employees Entering and Leaving the Office with Deep Learning Algorithms

EasyChair Preprint no. 6880

Abstract

This study attempts to create a system to monitor employees entering and leaving the office using face recognition. In addition, the system also signals by LED when recognizing a staff who has clearance to enter or notifies those who do not in the area. Events of entering and leaving from staff are written into a log file for management purposes. The face-detection and image preprocessing utilize Multi-task Cascaded Convolutional Network. Feature data is then extracted from the processed images by FaceNet, which is classified by the Support Vector Machine algorithm into a model. Information of employees and logs are saved in MySQL database, which is also used in a web application using Python and Django web framework.

Keyphrases: Cameras, Convolutional Neural Nets, Databases, detectors, Django, face, face detection, face recognition, FaceNet, feature extraction, learning (artificial intelligence), MySQL, Python, Raspberry Pi, real-time systems, Support Vector Machine, training

@Booklet{EasyChair:6880,
  author = {Viet Tran Hoang and Khoi Tran Minh and Nghia Dang Hieu and Viet Nguyen Hoang},
  title = {Monitoring Employees Entering and Leaving the Office with Deep Learning Algorithms},
  howpublished = {EasyChair Preprint no. 6880},

  year = {EasyChair, 2021}}