1. 吳家宇 吳明翰 Face Detection Based on Template Matching and 2DPCA Algorithm 2009/01/14

2. IntroductionIntroduction Face recognition Face detection Feature extraction Feature matching Related Methods Skin-based detection method Neural networks method SVM In this paper Template matching algorithm 2DPCA algorithm

3. Face Detection

4. First step Preprocess the image Normalize Histogram equalization Luminance compensation Second step Search rectangle regions Last step Detect every detection window hierarchically Face Detection Algorithm

5. FlowchartFlowchart

6. Resize the image – Resized into 400*300 pixel Histogram equalize the image Pre-ProcessingPre-Processing

7. Two-eye template Choose 30 standard face images and cut out a pair of eyes regions Get a 20*10 pixel two-eye template via calculating average number to many couples of eyes regions Face template Enlarged nearby based on the eyes template and construct 20*25 pixel face template Most non-face image blocks which interrelated coefficient is less than value T are discarded Template Matching Two-eye templateFace template Denotes separately a gray matrix average value Standard deviation

8. PCA – Matrices-to-vector conversion High dimensional vector space Difficult to evaluate covariance matrix Time-consuming 2DPCA – Directly computes eigenvectors of image covariance matrix – More efficiently than PCA – Easier to evaluate covariance matrix – Less time to determine the corresponding eigenvectors Comparison between PCA and 2DPCA

9. 2DPCA2DPCA

10. 2DPCA2DPCA

11. Minimal distance classifier – Realizes classification to every image matrix – Let – The detection windows corresponding to B are taken account of face region – Otherwise the detection windows are non-face Classification-Merging After two hierarchical detection – Most faces may be detected at multiple nearby positions or scales – Overlapping detected windows should be merged Merging

12. ComparisonComparison

13. ResultResult

14. ResultResult

15. Training Data Bao Face Database – Lots of face images, mostly people from Asia. Single face pictures are in the "one faces" subdirectory.

16. DemoDemo

17. DemoDemo