Summary of Automating Attendance Management in Human Resources: a Design Science Approach Using Computer Vision and Facial Recognition, by Bao-thien Nguyen-tat et al.
Automating Attendance Management in Human Resources: A Design Science Approach Using Computer Vision and Facial Recognition
by Bao-Thien Nguyen-Tat, Minh-Quoc Bui, Vuong M. Ngo
First submitted to arxiv on: 21 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Human-Computer Interaction (cs.HC); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents Haar Cascade, a machine learning algorithm that detects objects in images and videos using simple image processing techniques like edge detection and Haar features. Unlike deep learning algorithms, Haar Cascade is cost-effective and user-friendly, making it suitable for embedded computers like the NVIDIA Jetson Nano. The system uses Haar-like wavelets and advanced edge detection techniques to accurately detect and match faces in a database for attendance tracking. It aims to achieve high accuracy and robust performance while minimizing manual intervention and reducing errors. The integration of OpenCV2 and the NVIDIA Jetson Nano optimizes processing efficiency, making it suitable for resource-constrained environments. This system caters to a diverse range of educational institutions and workplace settings, democratizing attendance management technology. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to track who’s attending classes or work sessions using computers. It uses a special machine learning algorithm called Haar Cascade that can find faces in pictures and videos. Unlike some other algorithms that require powerful computers, this one is easy to use and doesn’t need expensive equipment. The system can match faces to names in a database, which helps with attendance tracking. It’s meant for schools, colleges, and workplaces to make sure people are showing up on time. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Tracking
