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系統識別號 U0026-2408202013272000
論文名稱(中文) 使用附加角度餘量的損失函數以及在正歸化特徵權重空間的線性判別分析實現深度人臉識別
論文名稱(英文) Deep Face Recognition using Additive Angular Margin Loss, and Linear Discriminant Analysis in Normalized Feature Weight Space
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 王怡媛
研究生(英文) Yi-Yuan Wang
學號 P76071137
學位類別 碩士
語文別 英文
論文頁數 74頁
口試委員 指導教授-連震杰
共同指導教授-郭淑美
口試委員-高宏宇
口試委員-陳洳瑾
口試委員-凃瀞珽
中文關鍵字 人臉識別  卷積神經網絡  角餘量損失  空間投影  線性判別分析 
英文關鍵字 face recognition  convolutional neural network  angular margin loss  spatial projection  linear discriminant analysis 
學科別分類
中文摘要 在人臉辨識這項課題中,我們會先使用深度學習的方法將人臉影像擷取出特徵。而這些特徵如果是來自同一個身份,通常會期望在特徵空間中,這些特徵的距離越近越好。反之,如果是來自不同身份,則距離越遠越好。最近的研究大多透過發展新的損失函數,來同時滿足壓縮類內距離和增加類間差距這兩個目的。這些研究也的確在人臉辨識中達到極高的準確度。
不過,當我們在現實情況下測試時,就會發現表現並不如我們預期,很大一部份的原因在於,現今開源的大型資料集大多內容都是以歐美人的影像為主,亞洲或是非洲臉孔相較之下非常少。而我們實驗主要是用來測試亞洲臉孔,因此有這樣的現象也不是那麼意外了。因次本篇論文就是希望在無法產生大量的亞洲臉孔資料來訓練人臉辨識中擷取特徵模型的情況下,解決以上的問題而設計出的方法。這項方法主要是透過線性判別分析和K-近鄰演算法來實現,也在測試中達到不錯的結果。
英文摘要 In the task of face recognition, we will first use deep learning method to extract features from face images. In the feature space, if these features come from the same identity, or we can say the same person, it is better to get smaller distance between them. Conversely, if they come from different identities, the farther away the better. Most of the recent studies have developed new loss functions for meeting meet two purposes simultaneously. One is compressing the in-tra distance and the other is increasing the in-ter distance. These studies have indeed achieved extremely high accuracy in face recognition.
However, when we test in reality, we will find that the performance is not as we expected. The main factor is that most of the large-scale open dataset are mainly based on European and American images, whether Asian or African face images are very few. And our experiment is mainly used to test Asian faces, so it is not so unexpected to have such a phenomenon. The purpose of this paper is to solve the above problem, in the case that we cannot generate a large number of Asian face data ourselves for training the feature extraction model in face recognition. Our method is realized by linear discriminant analysis and K-nearest neighbor algorithm, and it also achieves good results in the testing process.
論文目次 摘要 I
Abstract. II
誌謝 III
Table of Contents V
List of Figures VII
List of Tables X
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Related Works 3
1.3 Global Framework 6
1.4 Contribution 7
Chapter 2 System Setup and Function Specification 10
2.1 System Setup 10
2.2 Function and Hardware Specification 12
Chapter 3 Face Detection Using Multi-Task Cascaded Convolutional Networks 16
3.1 Training and Inference Processes of Multi-Task Cascaded Convolutional Networks 17
3.2 Multi-task Cascaded Convolutional Networks (MTCNN): P-Net, R-Net and O-Net 20
3.3 Non-Maximum Suppression for Filtering Overlapped Bounding Boxes 28
Chapter 4 Face Recognition Using Additive Angular Margin Loss and Linear Discriminant Analysis 32
4.1 Training and Inference Processes using Additive Angular Margin Loss with ResNet50 34
4.2 Feature Extraction Using ResNet 42
4.3 Normalized Feature Weight Space Creation Using Additive Angular Margin Loss Function 44
4.4 Recognition Refinement Using LDA and KNN in Normalized Feature Weight Space 49
Chapter 5 Experimental Results 53
5.1 Data Collection and Evaluation Metrics 53
5.2 Training with different dataset 59
5.3 Long tail effect 61
5.4 Cosine distance threshold 63
5.5 Spatial projection 65
5.6 Using PCA/LDA and KNN after Spatial projection 67
Chapter 6 Conclusion and Future Work 70
Reference 72
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