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Face Detection and Gender Recognition

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Face Detection and Gender Recognition. EE368 Project Report Michael Bax Chunlei Liu Ping Li 28 May 2003. Colour Spaces. RGB Colour-Space Histograms. HSV Colour-Space Histograms. Empirical PDF Approximation. Pixel Classification Error (RGB). Pixel Classification Error (HSV). Input Image. - PowerPoint PPT Presentation
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Face Detection Face Detection and Gender Recognition and Gender Recognition EE368 Project Report Michael Bax Chunlei Liu Ping Li 28 May 2003
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Page 1: Face Detection  and Gender Recognition

Face Detection Face Detection and Gender Recognitionand Gender Recognition

EE368 Project Report

Michael BaxChunlei Liu

Ping Li

28 May 2003

Page 2: Face Detection  and Gender Recognition

Colour SpacesColour Spaces

Page 3: Face Detection  and Gender Recognition

RGB Colour-Space HistogramsRGB Colour-Space Histograms

Page 4: Face Detection  and Gender Recognition

HSV Colour-Space HistogramsHSV Colour-Space Histograms

Page 5: Face Detection  and Gender Recognition

Empirical PDF ApproximationEmpirical PDF Approximation

Page 6: Face Detection  and Gender Recognition

Pixel Classification Error (RGB)Pixel Classification Error (RGB)

Page 7: Face Detection  and Gender Recognition

Pixel Classification Error (HSV)Pixel Classification Error (HSV)

Page 8: Face Detection  and Gender Recognition

Input ImageInput Image

Page 9: Face Detection  and Gender Recognition

Pixel Segmentation Pixel Segmentation Using the RGB Pixel PDFUsing the RGB Pixel PDF

Page 10: Face Detection  and Gender Recognition

Non-Face Object RemovalNon-Face Object Removal

Page 11: Face Detection  and Gender Recognition

Size-based Size-based Non-Face Object RemovalNon-Face Object Removal

Page 12: Face Detection  and Gender Recognition

Location-based Location-based Non-Face Object RemovalNon-Face Object Removal

Page 13: Face Detection  and Gender Recognition

Object Size Threshold CorrectionObject Size Threshold Correction

Page 14: Face Detection  and Gender Recognition

PCA-basedPCA-basedNon-Face Object RemovalNon-Face Object Removal

Page 15: Face Detection  and Gender Recognition

Connected Component Connected Component AnalysisAnalysis

Low pass filtering, hole filling and background rejection

Identification of connected faces based on statistical analysis

Iterative separation of connected regions

Preprocessing

Connected faces identification

Face separation

Page 16: Face Detection  and Gender Recognition

Connected ComponentsConnected Components

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Component SeparationComponent Separation

Page 18: Face Detection  and Gender Recognition

Separated ComponentsSeparated Components

Page 19: Face Detection  and Gender Recognition

Component IdentificationComponent Identification

Template matching and peak thresholding to remove remaining non-face objects

Removal of repeated faces segments using a distance constraint

Page 20: Face Detection  and Gender Recognition

Face Position RefinementFace 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

Page 21: Face Detection  and Gender Recognition

Image Pyramid-based Image Pyramid-based Template MatchingTemplate 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

Page 22: Face Detection  and Gender Recognition

Finding Faces Finding Faces with Template Matchingwith Template Matching

High threshold for accurate centre location

Moderate threshold for robust backup face location – if morphological

subsystem gives unexpected results

Page 23: Face Detection  and Gender Recognition

Gender DetectionGender Detection

Mean intensity Template matching

using average of each female face

Biased towards missing female faces to avoid false-positive penalty (9:1)

Page 24: Face Detection  and Gender Recognition

Face Detection ResultsFace Detection Results

Page 25: Face Detection  and Gender Recognition

Image Hits Repeated False Hits Distance Time (s) Bonus

1 21 0 0 11.1 91 2

2 24 0 0 15.6 90 2

3 25 0 0 10.5 97 0

4 24 0 0 11.8 97 1

5 24 0 0 10.7 103 0

6 24 0 0 9.6 94 0

7 22 0 0 11.2 88 1

Average 23.4 0 0 11.5 94 0.86

Results StatisticsResults Statistics

Page 26: Face Detection  and Gender Recognition
Page 27: Face Detection  and Gender Recognition

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Gender RecognitionFace Detection

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Gender RecognitionFace Detection


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