Title page for etd-1110117-150128


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URN etd-1110117-150128
Author Reui-yan Lin
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2017
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Video Summarization based on Face Recognition
Date of Defense 2017-10-29
Page Count 61
Keyword
  • Convolution Neural Network
  • Face Alignment
  • Face Recognition
  • Face Detection
  • Video Summarization
  • Abstract In recent year, the video surveillance for person identification has attracted increasing attention. Most of companies are reluctant to detect possible misbehavior from employee or intruder. They usually set up some control systems and surveillance cameras at entrance and exit. When the event of criminal had happened, one might need to spend huge human resource and a lot of time to identify the suspect information from the huge number of surveillance cameras. In view of above, this thesis presents a video summarization system based on face recognition. The system uses the face detection and recognition methods to find the time and the place where the target person appears. Moreover, the summarization method is used to record the information and organize out a summary video for quick view. To improve the accuracy of face detection and face recognition, we use the YOLO algorithm to process the face object detection and use the VGG-Face algorithm to recognize person object. Experimental results show that the proposed system has 81% accuracy in face detection. In face recognition, the proposed system has 96.82% accuracy in LFW dataset and has 99.16% accuracy in YTF dataset. Finally, the proposed system has the 92.45% precision rate and 98.98% recall rate in video summarization.
    Advisory Committee
  • Yau-Hwang Kuo - chair
  • Shiu-Ming Ko - co-chair
  • Jyh-Cheng Chen - co-chair
  • Chung-Nan Lee - advisor
  • Files
  • etd-1110117-150128.pdf
  • Indicate in-campus at 2 year and off-campus access at 2 year.
    Date of Submission 2017-12-10

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