Dissertation > Excellent graduate degree dissertation topics show

Research on Face Detection Based on Skin Color Segmentation and AdaBoost Algorithm

Author: ChenShiGang
Tutor: MaXiaoHu
School: Suzhou University
Course: Applied Computer Technology
Keywords: Multi- color segmentation Gaussian Haar-like features Strength characteristics Multi-threshold characteristics Face Detection
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 59
Quote: 0
Read: Download Dissertation


Face detection is a computer vision and pattern recognition of the most important research topics. Face detection in content-based image retrieval , virtual reality , video surveillance , face recognition and authentication , and many have a wide range of applications . The first condition is to use face recognition technology to detect human faces detected from the background , but the face by the light , background , posture and other factors , making the face detection become a complex and challenging research topic. For face detection face the problem , this paper focuses on the characterization of facial features facial feature detection methods and related algorithms , and make the appropriate improvements. In this paper, the research work include the following: ( a ) on a variety of color segmentation method is studied and improved multi- segmented Gaussian skin color segmentation. This method is mainly achieved through the following three steps : (a) in the three luminance Y range respectively CbCr skin color Gaussian fitting, the color of probability and statistics functions ; (b) select the appropriate threshold range , making it the skin contains cumulative probability distribution close to 100%; (c) using the corresponding threshold luminance range for image color segmentation . ( 2 ) through the edge detection operator calculation principle study, the proposed representative image contour information Haar-like intensity feature . Right Haar-like feature values ​​modulo operation can be Haar-like intensity eigenvalues ​​values. ( 3 ) By combining the basic threshold Haar-like feature and strength characteristics of the threshold value , the lower the error constructed multiple thresholds Haar-like feature . Haar-like feature combination of the basic characteristics and strength characteristics of the threshold value space Haar-like feature on the four divided regions obtained ; separately for each region of the negative sample weights for statistics, determines that the face region is a person or non-face , and set the corresponding flag. ( 4 ) use of color segmentation has been improved pretreatment cascade detector. In this method, the use of multi- Gaussian thresholding method for color image segmentation get quick color face candidate regions ; window color to be detected if more than a certain percentage , then use the cascade AdaBoost classifier to detect , or direct contracting for non- face .

Related Dissertations

  1. The Study of Human Face Detection Method Based on OpenCV,TP391.41
  2. Research on the Vision-Based Driver Fatigue Real-Time Detection,TP391.41
  3. Research and Implementation on Multi-angle Face Detection Technology Based on Continuous Adaboost Algorithm,TP391.41
  4. Study on Face Detection Based on AdaBoost Algorithm,TP391.41
  5. Research on Driver Fatigue Detection Based on Multi-feature Integration,TP391.41
  6. Research of Driver Fatigue Alarm Algorithm Based on Visual Information,TP391.41
  7. The Study Face Detecting Method Base on Gray Distribute,TP391.41
  8. Research and Implementation of Face Detection System Based on Android,TP391.41
  9. Research of Face Detection and Recognition System for Mobile Robot,TP391.41
  10. The Research of Face Detection and Recognition Technology Based on Color Image,TP391.41
  11. The Research of Face Detection Based on Skin Color Information and Adaboost Algorithm,TP391.41
  12. Research on Real-time Face Detection Based on Infrared Tracking,TP391.41
  13. Application Research of Embedded System Based on the FPGA,TP368.1
  14. Study on Face Recognition Technology Based on Embedded Access Control System,TP391.41
  15. Video target detection and tracking,TP391.41
  16. Facial Feature Eaxtraction Based on Skin Color Model and Principal Component Analysis,TP391.41
  17. Research of Face Detection Based on DSP,TP391.41
  18. Multi- pose face recognition based on subspace learning complex scenes,TP391.41
  19. Study of Skin Color Based Face Detection and Its Application in Redeye Removal,TP391.41
  20. Study of Automatic Redeye Removal Algorithm Based on Face Detection,TP391.41
  21. Research on Face Detection and Recognition Algorithm,TP391.41

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile