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HOG – Neural Network Based Student Attendance System

Aswin Rajeev1 , Jaiks Rebbi2 , Joseph Gavin Correya3 , Varun V Prabhu4 , Divya James5

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 1701-1705, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.17011705

Online published on May 31, 2019

Copyright © Aswin Rajeev, Jaiks Rebbi, Joseph Gavin Correya, Varun V Prabhu, Divya James . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Aswin Rajeev, Jaiks Rebbi, Joseph Gavin Correya, Varun V Prabhu, Divya James, “HOG – Neural Network Based Student Attendance System,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1701-1705, 2019.

MLA Style Citation: Aswin Rajeev, Jaiks Rebbi, Joseph Gavin Correya, Varun V Prabhu, Divya James "HOG – Neural Network Based Student Attendance System." International Journal of Computer Sciences and Engineering 7.5 (2019): 1701-1705.

APA Style Citation: Aswin Rajeev, Jaiks Rebbi, Joseph Gavin Correya, Varun V Prabhu, Divya James, (2019). HOG – Neural Network Based Student Attendance System. International Journal of Computer Sciences and Engineering, 7(5), 1701-1705.

BibTex Style Citation:
@article{Rajeev_2019,
author = {Aswin Rajeev, Jaiks Rebbi, Joseph Gavin Correya, Varun V Prabhu, Divya James},
title = {HOG – Neural Network Based Student Attendance System},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {1701-1705},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4474},
doi = {https://doi.org/10.26438/ijcse/v7i5.17011705}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.17011705}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4474
TI - HOG – Neural Network Based Student Attendance System
T2 - International Journal of Computer Sciences and Engineering
AU - Aswin Rajeev, Jaiks Rebbi, Joseph Gavin Correya, Varun V Prabhu, Divya James
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 1701-1705
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

With great advancements in technology, topics like machine learning, artificial intelligence, and big data are trending right now. These technologies are touching the lives of millions of people around the world. Data is being produced at exponential rates and computer engineers want to put this data to good use. In this project, we discuss the scope of machine learning and image processing to record the attendance of students in a classroom. This method is extremely efficient compared to the traditional attendance registration wherein the teacher has to manually mark the attendance of each and every student. With this system, the attendance is marked every hour and reports are generated automatically for easy consolidation. A combination of the HOG and Neural Networks are used for detection and recognition in our system. It eliminates the need for paper-based records and helps in quick consolidation.

Key-Words / Index Term

Facial Recognition; Image Processing; HOG; Neural Networks; Face API; Attendance System

References

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