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Face Detection and Gender Recognition Methodology Report with Statistical Results

This report presents a comprehensive methodology for face detection and gender recognition, covering various techniques such as colour space histograms (RGB and HSV), pixel classification errors, and input image pixel segmentation. It also addresses non-face object removal strategies, connected component analysis, and template matching for accurate face center identification. Furthermore, gender detection is discussed through the use of mean intensity and template matching. The report concludes with detailed results and statistical analysis of the methods implemented.

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Face Detection and Gender Recognition Methodology Report with Statistical Results

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  1. Face Detection and Gender Recognition EE368 Project ReportMichael BaxChunlei LiuPing Li 28 May 2003

  2. Colour Spaces

  3. RGB Colour-Space Histograms

  4. HSV Colour-Space Histograms

  5. Empirical PDF Approximation

  6. Pixel Classification Error (RGB)

  7. Pixel Classification Error (HSV)

  8. Input Image

  9. Pixel Segmentation Using the RGB Pixel PDF

  10. Non-Face Object Removal

  11. Size-based Non-Face Object Removal

  12. Location-based Non-Face Object Removal

  13. Object Size Threshold Correction

  14. PCA-basedNon-Face Object Removal

  15. Connected Component Analysis Preprocessing • Low pass filtering, hole filling and background rejection • Identification of connected faces based on statistical analysis • Iterative separation of connected regions Connected faces identification Face separation

  16. Connected Components

  17. Component Separation

  18. Separated Components

  19. Component Identification • Template matching and peak thresholding to remove remaining non-face objects • Removal of repeated faces segments using a distance constraint

  20. Face Position Refinement • The face centre is located at the bridge of the nose • The centroid of the segmented face is somewhat inaccurate in finding face centres • Multi-scale, high threshold template matching finds centres more accurately • Use centroid for remaining faces

  21. Image Pyramid-based Template Matching • Training face preprocessing • Training faces were rotation compensated, registered, and resampled in greyscale • Resampled faces were averaged and masked • Greyscale input image pyramid composition • 20% scale increments • Normalized cross-correlation with nose bridge-centred average face template

  22. Finding Faces with Template Matching • High threshold for accurate centre location • Moderate threshold for robust backup face location • if morphological subsystem gives unexpected results

  23. Gender Detection • Mean intensity • Template matching using average of each female face • Biased towards missing female faces to avoid false-positive penalty (9:1)

  24. Face Detection Results

  25. Results Statistics

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