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Title page for etd-0728110-152029


URN etd-0728110-152029 Statistics This thesis had been viewed 1993 times. Download 3 times.
Author Wei-hsu Chen
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2009 Semester 2
Degree Ph.D. Type of Document Doctoral Dissertation
Language zh-TW.Big5 Chinese Page Count 41
Title A Face Recognition System Using Facial Components and Local Features
Keyword
  • Active Shape Model
  • Face detection
  • Access control
  • Face recognition
  • Face recognition
  • Access control
  • Face detection
  • Active Shape Model
  • Abstract In general, people use portable cards or keys to get in and out from their home or office. However, cards and keys may cause many problems such as easily lost, embezzled, and distributed. This paper proposed a face recognition system based on five types of facial features. Adaboost and Active Shape Model are used to detect face firstly. False detected faces are removed by skin color and dynamic background modeling. In order to achieve high accuracy, the skew face is rotated for calibration. Side faces and false positive background are also removed. The five types facial features include statistical facial gradient, edge point projection profile, signature(length and angle) of facial component points, facial multi-width/height aspect ratio, and face template matching. All of these extracted features are used to match with the trained database and the weight is set according to the analysis of experimental results. Nearest neighbor classifier is deployed for face recognition by using each averaged feature point as the center. In experiments, the system is tested with 200 people from the database of MIT and ESSEX. Five images per person are used for training and totally 491 images are tested. The results show that the recognition rate is 98.3% and the processing speed reaches 220ms per frame with general personal computer.
    Advisor Committee
  • Chen-Chiung Hsieh - advisor
  • Files indicate in-campus access at 5 years and off-campus access at 10 years
    Date of Defense 2010-07-12 Date of Submission 2010-07-28


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