Automatic attendance system using facial recognition
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2022
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| Acesso em linha: | http://hdl.handle.net/10361/17212 |
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10361-172122022-09-13T21:01:43Z Automatic attendance system using facial recognition Sikdar, Tushar Binte Amin, Nafia Shupti, Ayesha Akter Ferdous, A.K.M Zubaer Dashgupta, Amit Arif, Hossain Department of Computer Science and Engineering, Brac University Automatic attendance system Facial recognition Human face recognition (Computer science) Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 28-31). Artificial Intelligence has brought revolutionary change all around the world prov ing its effectiveness almost in every aspect. AI is not only a science-fiction dream anymore, but also a constant part of our everyday lives. At present, computing is widely used to work smart and precise by eradicating human error and physical labour. An a System that will be recognizing face autonomously is a practical ap plication of AI which made life much easier. After we face the camera, it’ll Capture our photo and send it to the system, to check the database. If the system finds a match, it autonomously check that person’s attendance as present within the table and stores it in the database so that we can view the current version of the atten dance sheet. Throughout the paper we mainly used a Convolutional neural network and pretrained FaceNet model and we got an accuracy of approx. 94.85% using 100 different images. This paper proposes a quick face detection algorithm supported by a classifier, Support Vector Machines (SVM) which we used to separate more non face regions from the taken image. Face is identified by detecting eye and mouth region. The results demonstrate that the accuracy of the detection can be improved further by cutting down false detection. Tushar Sikdar Ayesha Akter Supti Nafia Binte Amin A.K.M Zubaer Ferdous B. Computer Science and Engineering 2022-09-13T08:41:51Z 2022-09-13T08:41:51Z 2022 2022-01 Thesis ID: 21301551 ID: 18101642 ID: 18101196 ID: 18101410 ID: 17101433 http://hdl.handle.net/10361/17212 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 31 Pages application/pdf Brac University |
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Brac University |
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Institutional Repository |
| language |
en_US |
| topic |
Automatic attendance system Facial recognition Human face recognition (Computer science) Artificial intelligence |
| spellingShingle |
Automatic attendance system Facial recognition Human face recognition (Computer science) Artificial intelligence Sikdar, Tushar Binte Amin, Nafia Shupti, Ayesha Akter Ferdous, A.K.M Zubaer Dashgupta, Amit Automatic attendance system using facial recognition |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Arif, Hossain |
| author_facet |
Arif, Hossain Sikdar, Tushar Binte Amin, Nafia Shupti, Ayesha Akter Ferdous, A.K.M Zubaer Dashgupta, Amit |
| format |
Thesis |
| author |
Sikdar, Tushar Binte Amin, Nafia Shupti, Ayesha Akter Ferdous, A.K.M Zubaer Dashgupta, Amit |
| author_sort |
Sikdar, Tushar |
| title |
Automatic attendance system using facial recognition |
| title_short |
Automatic attendance system using facial recognition |
| title_full |
Automatic attendance system using facial recognition |
| title_fullStr |
Automatic attendance system using facial recognition |
| title_full_unstemmed |
Automatic attendance system using facial recognition |
| title_sort |
automatic attendance system using facial recognition |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17212 |
| work_keys_str_mv |
AT sikdartushar automaticattendancesystemusingfacialrecognition AT binteaminnafia automaticattendancesystemusingfacialrecognition AT shuptiayeshaakter automaticattendancesystemusingfacialrecognition AT ferdousakmzubaer automaticattendancesystemusingfacialrecognition AT dashguptaamit automaticattendancesystemusingfacialrecognition |
| _version_ |
1814308475713355776 |