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Facial Recognition using Enhanced Facial Features k-Nearest Neighbor (k-NN) for Attendance System

Published: 18 September 2020 Publication History

Abstract

This paper discusses the developments of employee attendance system via face detection and facial recognition, using the enhanced featured supervised learning technique. The main goal of the proposed system, FaceAuth is to uniquely identify a person without the use of Internet connection and cloud services. The developed system can identify the face of a user using the k-nearest neighbors (k-NN) algorithm for both training and testing phases. Then, a suitable distance threshold is configured to exclude the unregistered person from the prediction. Meanwhile, it marks the attendance of individual, whenever the person's face has trained within five seconds. For 40 participants in this experiment, the developed system can achieve 92.5% in accuracy, without false positive error occur.

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Cited By

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  • (2024)Aplicación móvil para el control de asistencia de docentes universitarios con autenticación biométrica y verificación de geolocalizaciónMobile application for the attendance control of university professors with biometric authentication and geolocation verificationRevista Científica de Sistemas e Informática10.51252/rcsi.v4i2.6474:2(e647)Online publication date: 10-Jul-2024
  • (2024)Efficient Employee Attendance System Integrating RFID and Android-Based Face Recognition with Liveness Detection2024 International Conference on Electrical and Information Technology (IEIT)10.1109/IEIT64341.2024.10763296(163-168)Online publication date: 12-Sep-2024
  • (2022)Applications of Artificial Intelligence and Big Data in Industry 4.0 TechnologiesIndustry 4.0 Vision for Energy and Materials10.1002/9781119695868.ch5(121-158)Online publication date: 13-May-2022
  • Show More Cited By

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cover image ACM Other conferences
ITCC '20: Proceedings of the 2020 2nd International Conference on Information Technology and Computer Communications
August 2020
64 pages
ISBN:9781450375399
DOI:10.1145/3417473
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]

In-Cooperation

  • UPM: Universiti Putra Malaysia
  • Wuhan Univ.: Wuhan University, China

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 September 2020

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Author Tags

  1. Biometrics
  2. Face detection
  3. Facial recognition
  4. Machine learning
  5. Supervised learning
  6. k-nearest neighbors

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ITCC 2020

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Cited By

View all
  • (2024)Aplicación móvil para el control de asistencia de docentes universitarios con autenticación biométrica y verificación de geolocalizaciónMobile application for the attendance control of university professors with biometric authentication and geolocation verificationRevista Científica de Sistemas e Informática10.51252/rcsi.v4i2.6474:2(e647)Online publication date: 10-Jul-2024
  • (2024)Efficient Employee Attendance System Integrating RFID and Android-Based Face Recognition with Liveness Detection2024 International Conference on Electrical and Information Technology (IEIT)10.1109/IEIT64341.2024.10763296(163-168)Online publication date: 12-Sep-2024
  • (2022)Applications of Artificial Intelligence and Big Data in Industry 4.0 TechnologiesIndustry 4.0 Vision for Energy and Materials10.1002/9781119695868.ch5(121-158)Online publication date: 13-May-2022
  • (2021)Efficient Face Recognition System for Operating in Unconstrained EnvironmentsJournal of Imaging10.3390/jimaging70901617:9(161)Online publication date: 26-Aug-2021

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