臺灣博碩士論文加值系統

台灣四面環海，所以海岸巡防一直是我國國家安全之根本。通緝犯及非法走私集團，經常由西部海岸地區潛逃或走私至中國大陸與港澳地區。根據法務部調查局所提供的通緝犯影像，影像通常往往為舊照片且有時模糊不清，而通緝犯潛逃時經常會化妝與變裝，導致執法人員在人工辨識與查緝通緝犯人臉時有執勤上的困難。本文提出透過生成對抗網路與卷積網路技術，發展合成老化人臉與人臉辨識方法來協助進行通緝犯人臉偵測與辨識，藉此提高海巡署同仁於各項進、出港檢查時，或是執行通緝犯專案查緝時的目標人臉辨識效能。希望未來能將本論文發展的技術結合至海巡署已建置之智慧型港區監視、營區監視、環島監視系統及無人機即時影像等系統。除了可以大幅縮短海巡署同仁核對進、出港民眾的檢查時間外，更可降低人工檢查的錯誤率，進而提升通緝犯的查緝成效，以維國門之安全。

This thesis aims to provide a face image recognition system for identifying wanted criminals through deep learning and image recognition technology, which can be used in entry and exit inspects or special investigations for the Marine Patrol Department. In the future, this technology is expected to be integrated with the intelligent port surveillance, camp surveillance, round-island surveillance system, and drone real-time imaging systems that have been built by the Marine Patrol Administration. In addition to significantly reducing the inspection time, it can also reduce the error rate of manual inspections, thereby improving the performance of identifying wanted criminals and safeguarding the security of the country.

摘要 i
ABSTRACT ii
誌謝 iii
目錄 v
圖目錄 vi
表目錄 vii
第一章 緒論 1
第二章 相關研究 6
2.1 人臉老化相關之研究 6
2.2 人臉識別增強方法相關之研究 8
第三章 研究方法 10
3.1 卷積神經網路模型-LeNet 10
3.2 卷積網路模型-ResNet 13
3.3 卷積網路模型-MoibleNet 14
3.4 物件偵測模型-YOLO 14
3.5 人臉辨識經典模型-Haar Cascade Detector 16
3.6 人臉辨識模型-FaceNet 17
第四章 系統架構 19
第五章 實驗 24
5.1 實驗環境設定 24
5.2 實驗資料集與物件辨識指標 24
5.3 實驗結果 27
第六章 結論 34
參考文獻 35

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