Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3484824.3484896acmotherconferencesArticle/Chapter ViewAbstractPublication PagesdsmlaiConference Proceedingsconference-collections
research-article

Attendance System with Emotion Detection: A case study with CNN and OpenCV

Published: 13 January 2022 Publication History

Abstract

Multinational companies (MNC) take care of employees by taking feedback, in which each day's mood counts as a trivial factor. Traditional and common methods of collecting data are manual and take up employees productive time to complete. There is a solution that can help automate this process.
This study explores the implementation of Artificial Intelligence (AI) and Machine Learning (ML) models in an automated emotion and attendance system. The system is designed in such a way that companies can generate employee's mood/emotion data by facial recognition through a customized Convolutional Neural Network (CNN) model. The companies can place a camera on the entering and exit of each Block/Floor. Cameras can capture the image and provide input to OpenCV for easy integration and taking attendance. The captured image can further be used for mood detection and store in the database for each employee. The Human Resources (HR) department can track feedback from employee's facial expressions on the same day for social events, hikes, seminars, and new initiatives.

References

[1]
Arbib, M. A., & Fellous, J. (2004). Emotions: From brain to robot. Trends in Cognitive Sciences, 8(12), 554--561.
[2]
Atulapra. (n.d.). Atulapra/Emotion-detection. Retrieved from https://github.com/atulapra/Emotion-detection
[3]
Cao, X., Wang, Z., Yan, P., & Li, X. (2013). Transfer learning for pedestrian detection. Neurocomputing, 100, 51--57.
[4]
Challenges in Representation Learning: Facial Expression Recognition Challenge, (n.d.). Retrieved from https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data
[5]
Chauhan, D., & Goeduhub. (2020, August 10). Smart Attendance Tracking System using Face Recognition. Retrieved from https://www.goeduhub.com/10503/smart-attendance-tracking-system-using-face-recognition
[6]
Gupta, A. (2019, December 29). Emotion Detection: A Machine Learning project. Retrieved from https://towardsdatascience.com/emotion-detection-a-machine-learning-project-f7431f652b1f
[7]
Khandelwal, R. (2020, May 18). Convolutional Neural Network: Feature Map and Filter Visualization. Retrieved from https://towardsdatascience.com/convolutional-neural-network-feature-map-and-filter-visualization-f75012a5a49c
[8]
Kumar, A., Kaur, A., & Kumar, M. (2018). Face detection techniques: A review. Artificial Intelligence Review, 52(2), 927--948.
[9]
Shaees, S., Naeem, H., Arslan, M., Naeem, M. R., Ali, S. H., & Aldabbas, H. (2020). Facial Emotion Recognition Using Transfer Learning. 2020 International Conference on Computing and Information Technology (ICCIT-1441).
[10]
Sriratana, W., Mukma, S., Tammarugwattana, N., & Sirisantisamrid, K. (2018). Application of the OpenCV-Python for Personal Identifier Statement. 2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST).
[11]
Tang, T. (2020, June 20). Dynamic Emotion Detector using Transfer Learning. Retrieved from https://medium.com/swlh/dynamic-emotion-detector-using-transfer-learning-6856f3275c1a
[12]
Wu, D., Zhu, F., & Shao, L. (2012). One shot learning gesture recognition from RGBD images. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
[13]
Zahara, L., Musa, P., Wibowo, E. P., Karim, I., & Musa, S. B. (2020). The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of Micro-Expressions Face Using the Convolutional Neural Network (CNN) Algorithm based Raspberry Pi. 2020 Fifth International Conference on Informatics and Computing (ICIC).

Index Terms

  1. Attendance System with Emotion Detection: A case study with CNN and OpenCV

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      DSMLAI '21': Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence
      August 2021
      415 pages
      ISBN:9781450387637
      DOI:10.1145/3484824
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 13 January 2022

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Artificial Intelligence(AI)
      2. Attendance System
      3. Convolutional Neural Network(CNN)
      4. Machine Learning
      5. facial recognition
      6. mood/emotion detection

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      DSMLAI '21'

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 149
        Total Downloads
      • Downloads (Last 12 months)33
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 12 Feb 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media