Deep Learning Based 90-Degree Angle Side-View Face Recognition
| Autor: | Yuan-Cheng Liu, 劉原呈 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 In this paper, we explore 90-degree angle side-view face recognition based on deep learning. First, using the traditional machine learning method, the feature extraction is performed on the 90-degree side-view face image by MB-LBP and Haar-like respectively. Then, the 90-degree side-view face classifier is trained by Adaboost algorithm. On the other hand, the training and classification are performed with Faster R-CNN, one of deep learning methods. The experiment was carried out in three classification methods: MB-LBP feature classifier, Haar-like feature classifier and deep learning Faster R-CNN algorithm. Experimental results show that the depth learning Faster R-CNN algorithm has a success rate of more than 99% in the 90-degree side-view face without background interference images and in natural background images. In order to verify the possible effects in the case of background image interference, we also randomly placed the 90-degree side-view face image into the same natural background image for testing, which also can achieve good performance. Keywords: Side-view face, face recognition, machine learning, MB-LBP, Haar-like, feature extraction, deep learning, Faster R-CNN. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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