Machine learning approach for face recognition from 3D models generated by multiple 2D angular images
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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| Idioma: | English |
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Brac University
2019
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| Acesso em linha: | http://hdl.handle.net/10361/11758 |
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10361-11758 |
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10361-117582022-01-26T10:13:14Z Machine learning approach for face recognition from 3D models generated by multiple 2D angular images Mahmud, Moinuddin Mehzabin, Shegufta Prova, Sabrina Jahan Alam, Dr. Md. Ashraful Department of Computer Science and Engineering, Brac University Face recognition 3D model Motion algorithm Image processing--Digital techniques. Human face recognition (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Cataloged from PDF version of thesis. Includes bibliographical references (pages 53-56). We propose machine learning approach for face recognition from 3D models generated by multiple 2D angular images that recognizes faces from multiple angle of a 3D face model. Though, many works on identifying faces from 3D have already been done, there are many spaces to update, improve and contribute more features on previously done researches. However, this research includes SFM algorithm which is a combination of SIFT detector, Approximate Nearest Neighbors (ANN) algorithm and RANSAC algorithm to reconstruct 3D from multiple RGB images. Again, it includes AdaBoost Learning algorithm which was used to train model to recognize faces. Besides, we used Local Binary Pattern Histogram (LBPH) which is an effective texture administrator, marks the pixels of a picture by thresholding the area of every pixel. Finally, the System successfully recognizes faces which are deviated up to 60°angular deviation respectively to left and right (total: 120°). Additionally, it gives an accuracy of 80% to 100% depending on angular deviation of up to from 0°to 60°. Nevertheless, the rate of accuracy of our proposed system is reversely proportional to the Angular Deviation. Moinuddin Mahmud Shegufta Mehzabin Sabrina Jahan Prova B. Computer Science and Engineering 2019-04-24T08:36:12Z 2019-04-24T08:36:12Z 2018 2018-12 Thesis ID 14301119 ID 14201013 ID 14201011 http://hdl.handle.net/10361/11758 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. 56 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Face recognition 3D model Motion algorithm Image processing--Digital techniques. Human face recognition (Computer science) |
| spellingShingle |
Face recognition 3D model Motion algorithm Image processing--Digital techniques. Human face recognition (Computer science) Mahmud, Moinuddin Mehzabin, Shegufta Prova, Sabrina Jahan Machine learning approach for face recognition from 3D models generated by multiple 2D angular images |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Alam, Dr. Md. Ashraful |
| author_facet |
Alam, Dr. Md. Ashraful Mahmud, Moinuddin Mehzabin, Shegufta Prova, Sabrina Jahan |
| format |
Thesis |
| author |
Mahmud, Moinuddin Mehzabin, Shegufta Prova, Sabrina Jahan |
| author_sort |
Mahmud, Moinuddin |
| title |
Machine learning approach for face recognition from 3D models generated by multiple 2D angular images |
| title_short |
Machine learning approach for face recognition from 3D models generated by multiple 2D angular images |
| title_full |
Machine learning approach for face recognition from 3D models generated by multiple 2D angular images |
| title_fullStr |
Machine learning approach for face recognition from 3D models generated by multiple 2D angular images |
| title_full_unstemmed |
Machine learning approach for face recognition from 3D models generated by multiple 2D angular images |
| title_sort |
machine learning approach for face recognition from 3d models generated by multiple 2d angular images |
| publisher |
Brac University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/11758 |
| work_keys_str_mv |
AT mahmudmoinuddin machinelearningapproachforfacerecognitionfrom3dmodelsgeneratedbymultiple2dangularimages AT mehzabinshegufta machinelearningapproachforfacerecognitionfrom3dmodelsgeneratedbymultiple2dangularimages AT provasabrinajahan machinelearningapproachforfacerecognitionfrom3dmodelsgeneratedbymultiple2dangularimages |
| _version_ |
1814308059321729024 |