Masked face identification using face recognition
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| フォーマット: | 学位論文 |
| 言語: | English |
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Brac University
2024
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| オンライン･アクセス: | http://hdl.handle.net/10361/23942 |
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10361-239422024-08-29T21:03:26Z Masked face identification using face recognition Hossen, Tareq Uddin, Abbas Barua, Niloy Faik, Chowdhury Azmain Rhaman, Md. Khalilur Roy, Shaily Department of Computer Science and Engineering, Brac University Masked face Face recognition CNN MTCNN Deep learning ResNet V1 Cognitive learning theory Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages no. 47-48). This work intends to express one of the several well-known biometric authentications entitled Masked Face Identification models by applying current Face Recognition algorithms and public masked face raw data that predict beneficial use. At the end of 2019, the COVID-19 pandemic has been exotically expanding worldwide, which severely negatively harms people’s economies and well-being. Since using facial masks in social environments is now an efficient system to stop the spread of viruses, Nevertheless, appearance identification using facial masks is now a profoundly demanding duty because of the shortage of appropriate facial statistics. Here in our approach, the Deep Learning method will be executed by us to recognize the masked appearance by employing different face portions, some extra-superintendent and some owned-superintendent multi-task training facial appearance spotters, which can compact with different scales of appearance quickly and effectively. Additionally, the features are extracted by us from the masked face’s eyes, forehead, and eyebrow areas and merged with characteristics acquired from those methodologies into a combined structure for identifying masked faces. In order to process, we will perform various image processing techniques on our dataset to clean our data for better accuracy. We will train our model from scratch to perform face-mask recognition. The most important part of this project remains the data collection and data cleaning process. Using a data-centric approach, we will systematically enhance our data-set to improve accuracy and prevent overfitting by performing data augmentation and stratified sampling and keeping our model architecture constant. Finally, our proposed systems will be compared by us with multiple unions of genius appearance identification techniques among those advertised by CASIA, LFW, and owned gathered raw data, which are managed from different sources. When wearing a mask, a person’s face is hidden by 60–75%. Using only 30–40% of a person’s face, we designed a face mask recognition model with an accuracy of 99.84%. Trained on a modified CASIA dataset containing images with and without masks, the model could successfully get the embeddings of 85743 people within a few minutes and perform perfect face recognition with and without masks. Tareq Hossen Abbas Uddin Niloy Barua Chowdhury Azmain Faik B.Sc. in Computer Science 2024-08-29T05:02:49Z 2024-08-29T05:02:49Z 2022 2022-05 Thesis ID 18301133 ID 18301198 ID 18301087 ID 18101224 http://hdl.handle.net/10361/23942 en 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. 48 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Masked face Face recognition CNN MTCNN Deep learning ResNet V1 Cognitive learning theory Neural networks (Computer science) |
| spellingShingle |
Masked face Face recognition CNN MTCNN Deep learning ResNet V1 Cognitive learning theory Neural networks (Computer science) Hossen, Tareq Uddin, Abbas Barua, Niloy Faik, Chowdhury Azmain Masked face identification using face recognition |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Rhaman, Md. Khalilur |
| author_facet |
Rhaman, Md. Khalilur Hossen, Tareq Uddin, Abbas Barua, Niloy Faik, Chowdhury Azmain |
| format |
Thesis |
| author |
Hossen, Tareq Uddin, Abbas Barua, Niloy Faik, Chowdhury Azmain |
| author_sort |
Hossen, Tareq |
| title |
Masked face identification using face recognition |
| title_short |
Masked face identification using face recognition |
| title_full |
Masked face identification using face recognition |
| title_fullStr |
Masked face identification using face recognition |
| title_full_unstemmed |
Masked face identification using face recognition |
| title_sort |
masked face identification using face recognition |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23942 |
| work_keys_str_mv |
AT hossentareq maskedfaceidentificationusingfacerecognition AT uddinabbas maskedfaceidentificationusingfacerecognition AT baruaniloy maskedfaceidentificationusingfacerecognition AT faikchowdhuryazmain maskedfaceidentificationusingfacerecognition |
| _version_ |
1814308847412576256 |