The statistical learning methods in image processing and facial recognition
| dc.contributor.advisor | Bayat, Oğuz | |
| dc.contributor.author | Rafeeq, Sarbaz Omar | |
| dc.date.accessioned | 2022-04-28T11:30:57Z | |
| dc.date.available | 2022-04-28T11:30:57Z | |
| dc.date.issued | 2021 | en_US |
| dc.date.submitted | 2021 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | The aim of this thesis is to develop a new approach for The Statistical Learning Methods in image processing and Facial Recognition using the deep learning techniques in python. In the recent years there have been significant advances in face recognition by using deep neural networks. One of the potential next steps is to develop optimized 3D facial recognition. Shifting from 2D to 3D increases complexity of the problem by adding an- other dimension to data, making possible solutions more resource hungry. In this thesis we investigate different depth camera based facial recognition techniques and test their performance by deploying them on an embedded processor. We focus on applications for embedded systems and use a small lowresolution time of flight (ToF) camera with our system to keep overall system portable and compact. All faces images are then projected on the feature space (“face space”) to find the corresponding coordinators. The face space is composed of “Eigenfaces” or “Fisherfaces” which are actually eigenvectors found after doing a matrix composition - Eigen decomposition. At the heart of Eigenface method is the Principal Component Analysis (PCA) - one of the most popular unsupervised learning algorithms - while Fisherface is a better version of the previous one which makes use of both Principal Component Analysis and Linear Discrimination Analysis (LDA) to get more reliable results. Both methods would be examined deeply in their working principles as well as their potential applications in reality before coming to conclusion about the advantage and drawback of each one. In order to evaluate the performance of the developed techniques, we will create a dataset of 10 identities captured with a low-resolution depth camera and use it for both training and testing. The algorithms were realized by Python in Anaconda. Given initial images in the database, the program can detect and recognize the human faces in the provided pictures before saving them in the database to improve the calculation accuracy in the future. After evaluation, the recognition general results are exported on the screen with details included in the text files. | en_US |
| dc.identifier.citation | Rafeeq, S. O. (2021). The statistical learning methods in image processing and facial recognition (Yayınlanmış yüksek lisans tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/2389 | |
| dc.identifier.yoktezid | 711165 | |
| dc.institutionauthor | Rafeeq, Sarbaz Omar | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Facial Recognition | en_US |
| dc.subject | Low Computational Power | en_US |
| dc.subject | Linear Discrimination Analysis (LDA) | en_US |
| dc.title | The statistical learning methods in image processing and facial recognition | |
| dc.type | Master Thesis |
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