博碩士論文 107522627 詳細資訊




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姓名 哈帝恩(Fattah Azzuhry Rahadian)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 用於人臉驗證的緊湊且低成本的卷積神經網路
(Compact and Low-Cost CNN for Face Verification)
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摘要(中) 近年來,人臉驗證已廣泛用於保護網際網路上的各種交易行為。人臉驗證最先進的技術為卷積神經網絡(CNN)。然而,雖然CNN有極好的效果,將其佈署於行動裝置與嵌入式設備上仍具有挑戰性,因為這些設備僅有受限的可用計算資源。在本論文中,我們提出了一種輕量級CNN,並使用多種方法進行人臉驗證。首先,我們提出ShuffleNet V2的修改版本ShuffleHalf,並將其做為FaceNet算法的骨幹網路。其次,使用Reuse Later以及Reuse ShuffleBlock方法來重用模型中的特徵映射圖。Reuse Later通過將特徵直接與全連接層相連來重用可能未使用的特徵。同時,Reuse ShuffleBlock重用ShuffleNet V2(ShuffleBlock)的基本構建塊中第一個1x1卷積層輸出的特徵映射圖。由於1x1卷積運算在計算上很昂貴,此方法用於降低模型中1x1卷積的比率。第三,隨著通道數量的增加,卷積核大小增加,以獲得相同的感知域大小,同時計算複雜度更低。第四,深度卷積運算用於替換一些ShuffleBlocks。第五,將其他現有的現有算法與所提出的方法相結合,以查看它們是否可以提高所提出方法的性能 - 效率權衡。
在五個人臉驗證測試數據集的實驗結果表明,ShuffleHalf比其他所有方法都具有更高的準確度,並且只需要目前最先進的算法MobileFaceNet的48% FLOPs。通過Reuse ShuffleBlock重用特徵技術,ShuffleHalf的準確性得到進一步提高。該方法將計算複雜度降低到僅為MobileFaceNet的42% FLOPs。同時,改變卷積核大小和使用depthwise repetition都可以進一步降低計算複雜度,使MobileFaceNet的FLOPs只剩下38%,但效果依然優於MobileFaceNet。與一些現有方法的組合不會增加模型的準確性和性能 - 效率權衡。但是,添加shortcut連接和使用Swish激發函數可以提高模型的準確性,而不會顯著增加計算複雜度。
摘要(英) In recent years, face verification has been widely used to secure various transactions on the internet. The current state-of-the-art in face verification is convolutional neural network (CNN). Despite the performance of CNN, deploying CNN in mobile and embedded devices is still challenging because the available computational resource on these devices is constrained. In this paper, we propose a lightweight CNN for face verification using several methods. First, a modified version of ShuffleNet V2 called ShuffleHalf is used as the backbone network for the FaceNet algorithm. Second, the feature maps in the model are reused using two proposed methods called Reuse Later and Reuse ShuffleBlock. Reuse Later works by reusing the potentially unused features by connecting the features directly to the fully connected layer. Meanwhile, Reuse ShuffleBlock works by reusing the feature maps output of the first 1x1 convolution in the basic building block of ShuffleNet V2 (ShuffleBlock). This method is used to reduce the percentage of 1x1 convolution in the model because 1x1 convolution operation is computationally expensive. Third, kernel size is increased as the number of channels increases to obtain the same receptive field size with less computational complexity. Fourth, the depthwise convolution operations are used to replace some ShuffleBlocks. Fifth, other existing previous state-of-the-art algorithms are combined with the proposed method to see if they can increase the performance-efficiency tradeoff of the proposed method.
Experimental results on five testing datasets show that ShuffleHalf achieves better accuracy than all other baselines with only 48% FLOPs of the previous state-of-the-art algorithm, MobileFaceNet. The accuracy of ShuffleHalf is further improved by reusing the feature. This method can also reduce the computational complexity to only 42% FLOPs of MobileFaceNet. Meanwhile, both changing kernel size and using depthwise repetition can further decrease computational complexity to only 38% FLOPs of MobileFaceNet with better performance than MobileFaceNet. Combination with some existing methods does not increase the accuracy nor performance-efficiency tradeoff of the model. However, adding shortcut connections and using Swish activation function can improve the accuracy of the model without any noticeable increase in the computational complexity.
關鍵字(中) ★ 人臉驗證
★ 輕量級
★ 卷積神經網絡
★ 複雜度
關鍵字(英) ★ face verification
★ lightweight
★ convolutional neural network
★ complexity
論文目次 抽象 i
Abstract ii
Acknowledgments iii
Contents iv
List of Figures vii
List of Tables viii
一、Introduction 1
1.1 Background 1
1.2 Problem Formulation 2
1.3 Research Objectives 2
1.4 Research Originality 3
二、Literature Review 4
三、Theoretical Basis 9
3.1 Computer Vision 9
3.2 Face Recognition 9
3.3 Artificial Neural Network 9
3.3.1 Perceptron 10
3.3.2 Activation function 11
3.3.3 Structure 12
3.3.4 Training method: forward propagation and backpropagation 13
3.3.5 Parameter initialization 15
3.3.6 Loss function 16
3.3.7 Optimizer 17
3.4 Convolutional Neural Network 18
3.4.1 Lightweight CNN (manual and automatic) 22
3.5 Normalization 25
3.6 Regularization 26
3.6.1 Dropout 27
3.6.2 DropBlock 28
3.7 Data Augmentation 29
3.8 Skip Connections 29
3.9 Squeeze-Excitation Module 30
3.10 Octave Convolution 31
3.11 Siamese Network 32
3.12 Object Detection 33
3.13 Model Evaluation 34
四、Research Methodology 36
4.1 Literature Study 36
4.2 Tools and Materials 36
4.2.1 Tools 36
4.2.2 Materials 37
4.3 Research Procedure 37
4.3.1 Research activities 37
4.3.2 General description of the model 38
4.3.3 Preprocessing 38
4.3.4 Architecture design 40
4.4 Additional Proposed Methods 45
4.4.1 Feature reuse 45
4.4.2 Kernet 48
4.4.3 Depthwise repetition 49
4.4.4 Other methods 49
4.5 Model Evaluation 49
五、Results and Discussion 51
5.1 Experiment Results for Baselines and ShuffleHalf 51
5.2 Experiment Results for Feature Reuse on ShuffleHalf 52
5.3 Experiment Results for Kernet, Depthwise Repetition, and Swish 55
5.4 Experiment Results for Other Methods 57
六、Conclusion 60
Bibliographies 62
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指導教授 王家慶(Jia-Ching Wang) 審核日期 2019-7-29
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