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

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

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

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

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

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

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

8. 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.

9. 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.

10. Neural Network Classification EE368 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.

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