本研究探討基於深度學習之90度側臉辨識。首先使用傳統機器學習方法，分別以MB-LBP與Haar-like 對90度側臉影像進行特徵擷取。再以Adaboost 演算法訓練出90度側臉分類器。另一方面，使用深度學習方法中的Faster R-CNN進行訓練與分類。
實驗以三種分類方法來進行：MB-LBP特徵分類器、Haar-like特徵分類器與深度學習Faster R-CNN 演算法。實驗結果顯示深度學習Faster R-CNN 演算法在90度側臉在無背景干擾影像與在自然背景影像中的偵測成功率皆達99%以上。為了驗證在背景影像的干擾的情況下的可能影響，我們也將90度側臉影像隨機貼入具相同自然背景影像中做測試，也能達到良好的效能。

關鍵字：90度側臉，人臉辨識，機械學習，MB-LBP，Haar-like，特徵擷取，深度學習，Faster R-CNN。

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.

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