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研究生:林柏仰
研究生(外文):LIN, BO-YANG
論文名稱:基於Inception-Resnet-v2深度卷積神經網路之即時人臉識別
論文名稱(外文):Real-time Face Recognition Based on Inception-ResNet-v2 Deep Convolution Neural Network
指導教授:李仁軍
指導教授(外文):LEE, JEN-CHUN
口試委員:雷伯瑞林聖義潘彥良黃煌初李仁軍
口試委員(外文):LEI, PO-RUEYLIN, SENG-YIPAN, YEN-LIANGHUANG, HUANG-CHULEE, JEN-CHUN
口試日期:2020-07-28
學位類別:碩士
校院名稱:國立高雄科技大學
系所名稱:電訊工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:68
中文關鍵詞:深度學習人臉辨識人臉偵測
外文關鍵詞:Deep learningface recognitionface detection
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人臉辨識領域近年來發展逐漸飽和除了改善網路結構,改善損失函數也是一種方法,也讓卷積神經網路從資料中學習到更多的特性,由於深度學習演算法的突破,漸漸發展出基於深度學習的人臉辨識,因此本研究提出「Inception-ResNet-v2」深度卷積神經網路於人臉即時辨識,人臉識別主要包含四個重要步驟:人臉偵測、影像前處理、特徵擷取、特徵比對。
人臉偵測部分,我們採用快速且錯誤率低的多工聯結卷積網路(Multi-Task Cascaded Convolutional Networks , MTCNN),影像前處理使用人臉對齊透過數據增強(Data Augmentation)的方法進行,使訓練資料多樣化,提升學習模型對未知資料的適應能力。辨識方面透過深度卷積神經網路,提取訓練資料特徵,透過歐式距離進行特徵比對與人臉分類器實現人臉即時識別。
本研究有二個主要貢獻,第一個貢獻是在Inception-ResNet-v1、Inception-ResNet-v2、Inception-v3和Inception-v4四種深度卷積神經網路在softmax loss with center loss損失函數下,得出Inception-ResNet-v1訓練速度最快和Inception-ResNet-v2效果最佳。第二個貢獻是使用預訓練模型經過triplet loss微調後準確率提升。

In recent years, face recognition studies have been developed to full extent and become saturated. It is also important to be noted that this technique can also be advanced by optimizing Loss function in addition to improving the infrastructure of current network. Furthermore, the breakthrough arithmetic methods on using “Deep Learning” to learn much complex features which describe human’s face are flourishing as well. In this study, we include four major steps to accomplish the magnificent result of “Inception-ResNet-v2” of Deep Convolutional Neural Network(DCNN) on face recognition : face detection, pre-image management , features extraction and feature comparison.
Face detection, a speedy and low error rate network, Multi-Task Cascaded Convolutional Networks (MTCNN) is adopted in dealing with this job. pre-image management, it is divided into two groups: training and testing schemes. Both of them not only provide the necessary data and data augmentation for the DCNN, but also diversify the data, which result in promoting and increasing adaptability for our learning model to unknown new data. face recognition, by means of DCNN, the characteristics of related training can be obtained. Meanwhile, by virtue of Euclidean Distance operated in the comparison of unique characteristics, the practice and realization of real-time face recognitions can be achieved.
This research has two main contributions. The first contribution is obtained under the softmax loss with center loss function of the four deep convolutional neural networks Inception-ResNet-v1, Inception-ResNet-v2, Inception-v3 and Inception-v4. Inception-ResNet-v1 has the fastest training speed and Inception-ResNet-v2 has the best effect. The second contribution is the use of pre-trained model after triplet loss fine-tuning to improve accuracy.

目錄
中文摘要……………………………………………………………………………….i
Abstract………………………………………………………………………………ii
誌謝…………………………………………………………………………………...iii
目錄………………………………………………………………………………….. iv
圖目錄………………………………………………………………………………...vi
表目錄……………………………………………………………………………….viii
第一章 緒論.............................1
1.1研究背景.............................1
1.2研究動機.............................1
1.3研究目的.............................2
1.4研究方法.............................3
1.5研究架構.............................4
1.6論文架構.............................5
第二章 文獻探討..........................6
2.1 文獻回顧............................6
2.2 深度學習協助影像處理識別.............6
2.3 人臉相關前處理技術..................10
2.3.1 數據增強....................10
2.3.2 影像正規化處理...............13
2.3.3 OpenCV仿射變換..............14
2.4 人臉偵測相關技術....................16
2.4.1 OpenCV人臉偵測..............16
2.4.2 Dlib人臉偵測................18
2.4.3 人臉關鍵點偵測...............19
2.5 人臉識別演算法......................23
2.5.1 傳統特徵擷取..................23
2.5.2 卷積神經網路之特徵擷取.........24
2.5.3 特徵空間.....................27
2.5.4 卷積神經網路損失函數..........28
第三章 人臉識別方法.....................33
3.1 MTCNN人臉偵測......................34
3.1.1 NMS演算法.....................37
3.2人臉預處理..........................37
3.2.1人臉對齊.......................37
3.3人臉特徵之深度卷積神經網路............38
3.3.1 Inception組成結構.............39
3.3.2 Inception-ResNet組成結構......43
3.3.3 Bottleneck結構...............48
3.4特徵比對............................50
3.4.1 歐式距離......................50
3.5人臉即時識別.........................51
3.5.1 人臉訓練模型結構................51
3.5.2 支援向量機.....................52
第四章 實驗結果與分析....................53
4.1實驗結果.............................53
4.1.1人臉資料庫......................54
4.1.2超參數與訓練....................54
4.2實驗過程.............................55
4.3效能評估.............................56
4.3.1系統速度........................57
4.4比較與討論...........................58
第五章 結論與未來展望....................63
5.1 結論...............................63
5.2 未來展望...........................64
參考文獻...............................65


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