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Face Detection In Color Images

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.

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Face Detection In Color Images

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  1. EE368 Project Face Detection In Color Images Wenmiao Lu Shaohua Sun Group 3

  2. EE368 Project Skin Segmentation • Overview • Human Skin Segmentation • Adaptive Shape Analysis • View-based Face Detection • Results Shape Analysis Face Detection

  3. EE368 Project Human Skin Segmentation • Use YCbCr color space for good cluster separation • Model the skin and background color distributions with GMM • Segmentation by maximum likelihood classification

  4. EE368 Project An Example for Initial Skin Segmentation Fairly complete skin segmentation with some noise

  5. EE368 Project Adaptive Shape Analysis Refine the binary map Open to get smaller regions Initial Face Identification Different Structuring Elements Prior Information Erosion & Dilation

  6. EE368 Project An Example for Adaptive Shape Analysis • Medium size: faces • Small, big or odd shaped regions: passed to next stage

  7. EE368 Project View-Based Face Detection Test Pattern Project to Low-dimensional Feature Space Spanned by Largest Eigenvectors Face/Non-Face Decision

  8. EE368 Project Distances to Face Model 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.

  9. EE368 Project Distances between Test Pattern and One Cluster *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.

  10. EE368 Project Neural Network Classification • 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.

  11. EE368 Project Experimental Results Detection Rate: 95.6% False Positive: 0.6%

  12. EE368 Project

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