dc.contributor.advisor | Arif, Hossain | |
dc.contributor.author | Sikdar, Tushar | |
dc.contributor.author | Binte Amin, Nafia | |
dc.contributor.author | Shupti, Ayesha Akter | |
dc.contributor.author | Ferdous, A.K.M Zubaer | |
dc.contributor.author | Dashgupta, Amit | |
dc.date.accessioned | 2022-09-13T08:41:51Z | |
dc.date.available | 2022-09-13T08:41:51Z | |
dc.date.copyright | 2022 | |
dc.date.issued | 2022-01 | |
dc.identifier.other | ID: 21301551 | |
dc.identifier.other | ID: 18101642 | |
dc.identifier.other | ID: 18101196 | |
dc.identifier.other | ID: 18101410 | |
dc.identifier.other | ID: 17101433 | |
dc.identifier.uri | http://hdl.handle.net/10361/17212 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 28-31). | |
dc.description.abstract | 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. | en_US |
dc.description.statementofresponsibility | Tushar Sikdar | |
dc.description.statementofresponsibility | Ayesha Akter Supti | |
dc.description.statementofresponsibility | Nafia Binte Amin | |
dc.description.statementofresponsibility | A.K.M Zubaer Ferdous | |
dc.format.extent | 31 Pages | |
dc.language.iso | en_US | en_US |
dc.publisher | Brac University | en_US |
dc.rights | 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. | |
dc.subject | Automatic attendance system | en_US |
dc.subject | Facial recognition | en_US |
dc.subject.lcsh | Human face recognition (Computer science) | |
dc.subject.lcsh | Artificial intelligence | |
dc.title | Automatic attendance system using facial recognition | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B. Computer Science and Engineering | |