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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
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Abstract


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 .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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