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Attendance System Using Face Recognition Library

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Applied Information Processing Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1354))

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Abstract

Among several methods used for monitoring the attendance of students, facial recognition is not mostly acclaimed. The emerging image processing technology is not a prevailing part of regular attendance monitoring systems regardless of the numerous benefits. To eliminate data handling processes, it is required to design an intelligent system that detects a student’s face and verifies it from the database. This paper proposes a system that uses TensorFlow for face identification and verification and displays students’ attendance on a web-based/local GUI. This system is capable of generating real-time output based on video feed obtained from the classroom. The outcome is labeled with the name of the student as entered in the database. This system functions on the Google Colab platform on Graphics Processing Units (GPUs). In its preliminary stage, a local dataset of a student under diverse light conditions has been experimented upon to study the behavior of the Face Recognition algorithm in illumination. The results suggest that the algorithm is effective under low light conditions as well. This paper primarily engenders significant advances in image processing through facial recognition library highlighting Machine Learning applications in everyday circumstances.

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Abbreviations

GUI:

Graphical User Interface

GPU:

Graphics Processing Unit

HOG:

Histogram of Oriented Gradients

SVM:

Support Vector Machine

PSNR:

Peak Signal-to-Noise Ratio

RGB:

Red Green Blue

SQL:

Structured Query Language

CNN:

Convolutional Neural Network

CVPR:

Computer Vision and Pattern Recognition

VGGF:

Very Deep Convolutional Network for Large-Scale Face Recognition Dataset

PCA:

Principal Component Analysis

LBPH:

Local Binary Pattern Histogram

LDA:

Linear discriminant analysis

API:

Application Programming Interface

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Author information

Authors and Affiliations

  1. Department of Electronics & Telecommunication, MCT’s Rajiv Gandhi Institute of Technology, Mumbai, Maharashtra, India

    Bhavna Patel, Vedika Patil, Onkar Pawar, Omkar Pawaskar & J. R. Mahajan

Authors
  1. Bhavna Patel
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  2. Vedika Patil
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  3. Onkar Pawar
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  4. Omkar Pawaskar
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  5. J. R. Mahajan
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Editor information

Editors and Affiliations

  1. Department of Electronics and Telecommunications Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, India

    Brijesh Iyer

  2. Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Debashis Ghosh

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Patel, B., Patil, V., Pawar, O., Pawaskar, O., Mahajan, J.R. (2022). Attendance System Using Face Recognition Library. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_23

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