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  4. Face Detection by Outline, Color, and Facial Features
 
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Face Detection by Outline, Color, and Facial Features

Date Issued
2010
Date
2010
Author(s)
Liao, Ke-Jie
URI
http://ntur.lib.ntu.edu.tw//handle/246246/253176
Abstract
Automatic human face recognition is a very important subject in pattern recognition fields. This is due to the fact that face has less privacy problems against other identification systems such as iris recognition. Before doing further processing, we need first to segment the face from a given image and it is still a very difficult problem. Face detection systems can be roughly classified into two approaches, feature-based and holistic-based. In holistic-based method, given image is processed without analyzing it into several smaller features. It has the advantage of finding small faces and faces in poor-quality images. On the other side, feature-based method is first to detect the existence of facial features in the given image and then based on the extracted features to declare whether a face is presented. In this thesis, we will concern the problem of finding a face in a still color image and proposed a feature-based face detection scheme. Our scheme is first to find skin-like pixels and group them based on spatial relation. Each disjoint skin-like region will be examined to see whether the boundary of it is like an ellipse or not. If the answer is yes, the region will further processed to check whether it contains a valid facial feature triangle. For doing this, we combined color, fuzzy logic and Gabor wavelets to detect eye candidates and mouth candidates. Valid triangles will be stored and used for displaying the location of the face with labeled eyes and mouth. In our experimental results based on a subset of Caltech database, our processing time for detecting faces in a color image with size is in average seconds (Intel Q6600 2.4GHz). The detection rate is 95% while only one false positive is found. The thesis is organized as below. In chapter 2-4, we will review some pattern recognition algorithms. In chapter 5-10, we will discuss our proposed face detection system in deep details.
Subjects
Pattern recognition
automatic face detection
HSI Baysian Model
ellipse estimation
principle component analysis
fuzzy logic
triangular norms
Gabor wavelets
the Hough transform
Type
thesis
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