face detection in color images
DESCRIPTION
EE368 Project. Face Detection In Color Images. Wenmiao Lu Shaohua Sun. Group 3. EE368 Project. Skin Segmentation. Overview Human Skin Segmentation Adaptive Shape Analysis View-based Face Detection Results. Shape Analysis. Face Detection. EE368 Project. - PowerPoint PPT PresentationTRANSCRIPT
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Face Detection In Color Images
Wenmiao Lu Shaohua Sun
Group 3
EE368 Project
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•Overview
•Human Skin Segmentation
•Adaptive Shape Analysis
•View-based Face Detection
•Results
EE368 Project
Skin
Segmentation
Shape Analysis
Face Detection
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Human Skin SegmentationEE368 Project
•Use YCbCr color space for good cluster separation
•Model the skin and background color distributions with GMM
•Segmentation by maximum likelihood classification
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An Example for Initial Skin Segmentation
EE368 Project
Fairly complete skin segmentation with some noise
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Adaptive Shape AnalysisEE368 Project
Refine the binary map
Open to get smaller regions
Initial Face Identification
Erosion & Dilation Different
Structuring Elements
Prior Information
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An Example for Adaptive Shape Analysis
EE368 Project
•Medium size: faces
•Small, big or odd shaped regions: passed to next stage
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View-Based Face Detection
EE368 Project
Project to Low-dimensional Feature Space Spanned by Largest Eigenvectors
Face/Non-Face DecisionTest Pattern
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Distances to Face Model
EE368 Project
Test pattern is measured against the Face Model, which consists of
i) 6 Face Clusters and ii) 6 Non-face Clusters*Figure is obtain from Sung, Kah Kay (1996)Learning and Example Selection for Object and Pattern Detection.Ph.D. Thesis, Massachusetts Institute of Technology, 1995.
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Distances between Test Pattern and One Cluster
EE368 Project
*Figure is obtain from Sung, Kah Kay (1996)Learning and Example Selection for Object and Pattern Detection.Ph.D. Thesis, Massachusetts Institute of Technology, 1995.
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Neural Network ClassificationEE368 Project
• 2-distance metric is discriminative for face and non-face patterns.
• 2 distances have different magnitude; neural network performs the final classification. *Figure is obtain from Sung, Kah Kay (1996)
Learning and Example Selection for Object and Pattern Detection.Ph.D. Thesis, Massachusetts Institute of Technology, 1995.
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Experimental Results
Detection Rate: 95.6% False Positive: 0.6%
EE368 Project
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EE368 Project
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