本篇論文展示一個基於孿生網路、利用混合架構的離線簽名辨識系統。
我們採用殘差網路的結構擔當特徵擷取的模型，使WI模式下的特徵能更有
效率的學習。另外，我們使用單層的孿生網路結構擔任WD模式下的分類
器，藉此節省儲存空間。為了減少簽名的內在變異度與確保網絡能夠更有效
的學習，我們提出選擇範例的方式作為孿生網絡的其中一個輸入，並利用
選擇範例過程中得到的資訊，引入範例選擇項來提升模型的準確度。透過
使用提出的範例選擇與適應性對比損失函數的系統，比起未使用的系統提
高了5.9%。基於GPDS簽名資料庫，此系統能夠得到比當前最新技術還要好
的94.61%正確率。

This paper presents an off-line handwritten signature verification system
based on the Siamese network, where a hybrid architecture is used. The Residual
neural Network (ResNet) is used to realize a powerful feature extraction
model such that Writer Independent (WI) features can be effectively learned.
A single-layer Siamese Neural Network (NN) is used to realize a Writer Dependent
(WD) classifier such that the storage space can be minimized. For
the purpose of reducing the impact of the high intraclass variability of the
signature and ensuring that the Siamese network can learn more effectively,
we propose a method of selecting a reference signature as one of the inputs
for the Siamese network. To take full advantage of the reference signature, we
modify the conventional contrastive loss function to enhance the accuracy. By
using the proposed techniques, the accuracy of the system can be increased by
5.9%. Based on the GPDS signature dataset, the proposed system is able to
achieve an accuracy of 94.61% which is better than the accuracy achieved by
the current state-of-the-art work.

1 Introduction 1
2 Preliminaries 8
2.1 Hybrid System . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Convolutional Neural Network . . . . . . . . . . . . . . . . . 9
2.3 ResNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 WI Feature Learning based on the Siamese CNN Network . . . . 12
3 Proposed Hybrid Signature Verification System 14
3.1 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2 Reference Selection . . . . . . . . . . . . . . . . . . . . . 17
3.2.1 Loss Function . . . . . . . . . . . . . . . . . . . . . . . 17
3.2.2 Writer-Independent Feature Learning . . . . . . . . . . . . .20
3.2.3 Writer-dependent classifier . . . . . . . . . . . . . . . .. 23
4 Performance Evaluation 26
4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2 Experimental Protocol . . . . . . . . . . . . . . . . . . . . 27
4.3 Performance Metrics . . . . . . . . . . . . . . . . . . . . . .29
4.4 Results and Discussions . . . . . . . . . . . . . . . . . . . .29
5 Conclusion 39
Bibliography 40

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