基於深度卷積神經網路移轉學習技術的臉面辨識系統

Face Recognition with Transfer Learning Approach in Deep CNN

夏奇泰(Chetan Sharma)

指導教授 ： 江正雄

淡江大學/工學院/電機工程學系碩士班/碩士(2018年)

https://doi.org/10.6846/TKU.2018.00745

摘要

近年來，機器學習和深度學習受到了高度的關注，特別是在與使用深度學習相關的分類，例如：資料探勘，人臉和語音辨識等。其中性能的提升主要是由於複雜的演算法和架結構，部分原因則歸功於好的數據資料。本論文的主要動機是將卷積神經網路(CNN)用於人臉辨識，其目的是透過轉移學習 (Transfer Learning)，使用新數據訓練預先訓練模型(Pre-trained Model)的方法，進而獲得正確的預測和準確的分類結果，在三個分類的訓練資料庫中，各有兩百張圖片。這個訓練資料庫是用來進一步訓練改良的預先訓練模型(pre-trained model) ，進而可以在不同的情境下測試資料庫影像，並在各別的情境下得到準確的預測輸出

關鍵字

CNN ； Transfer learning ； Face recognition ； Alex net

並列摘要

Machine learning and deep learning particularly have gained a lot of attention in recent years, especially for classification related tasks, such as text mining, face and speech, etc. The performance increase is mostly due to complex algorithm and architecture, and partly due to the use of good data sets. The main motivation of this thesis is to train a Convolutional Neural Network (CNN) based system for face recognition aiming at positive prediction and appreciative accuracy result. By way of transfer learning, a pre-trained model can be tailored for different applications with new data. The resulting output attains good accuracy and result in different cases. The objective is to differentiate 3 labeled categories, each with 200 images in the training dataset. The training data is provided to modify the pre-trained model, which is further classified with the test images in different scenarios, where the prediction results achieve high accuracy for each individual case.

並列關鍵字

CNN ； Transfer learning ； Face recognition ； Alex net

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