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Face Detection. EE368 Final Project Spring 2003. - Group 6 - Anthony Guetta Michael Pare Sriram Rajagopal. Overview. Problem Identification Methods Adopted Color Segmentation Morphological Processing Template Matching EigenFaces Gender Classification. Color Segmentation.

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Face Detection


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    1. Face Detection EE368 Final Project Spring 2003 - Group 6 - Anthony Guetta Michael Pare Sriram Rajagopal

    2. Overview • Problem Identification • Methods Adopted • Color Segmentation • Morphological Processing • Template Matching • EigenFaces • Gender Classification

    3. Color Segmentation • Use the color information • Two approaches: • Global threshold in HSV and YCbCr space using set of linear equations. Lot of overlap exists (a) (b) Clustering in (a) YCbCr and (b) V vs. H space. Red is non-face and blue is face data

    4. Result of color segmentation using Global thresholding

    5. Overlap exists in RGB space also • Second approach involves RGB vector quantization (Linde, Buzo, Gray) • Use RGB as a 3-D vector and quantize the RGB space for the face and non-face regions Sample Blue vs Green plot for face (blue) and non-face (red) data.

    6. Results from initial quantization • Common problems identified

    7. Better Code book developed • Problem areas broken up

    8. Initial step of open and close performed to fill holes in faces • Elongated objects removed by check on aspect ratio and small areas discarded

    9. Morphological Processing • Segmented and processed Image consists of all skin regions (face, arms and fists) • Need to identify centers of all objects, including individual faces among connected faces • Repeated EROSION is done with specific structuring element

    10. Previous state stored to identify new regions when split occurs Superimposed mask image with eroded regions for estimate of centroids

    11. Template Matching • Data set has 145 male and 19 female faces • Need to identify region around estimated centroids as face or non-face • Multi-resolution was attempted. But distortion from neighboring faces gives false values • Smaller template has better result for all face shapes • Template used is the mean face of 50x50 pixels Mean Face used for template matching

    12. Illumination problem identified • Top has low lighting, lower part is brighter • Left and right edges of images do not have people • 2-D weighting function for correlation values applied 2-D weighting function Sample correlation result

    13. Result from template matching and thresholding. Rejected - Red ‘x’. Detected Faces – Green ‘x’

    14. EigenFace based detection • Decompose faces into set of basis images • Different methods of candidate face extraction from image EigenFaces (b) (a) Candidate face extraction (a) Conservative (b) multi-resolution with side distortion

    15. Sample result of eigenface. Red ‘+’ is from morphological processing and green ‘O’ is from eigenfaces

    16. Minimum Distance between vector of coefficients to that of the face dataset was the metric. • It depends very much on spatial similarity to trained dataset • Slight changes give incorrect results • Hence, only template matching was used

    17. Gender classification • Eigenfaces and template matching for specific face features do not yield good results • Other features for specific females were used – the headband • Template matching was performed for it • Conservative estimate was done to prevent falsely identifying males as a female The headband template

    18. Training Image Final Score Detect Score Number Hits Num Repeat Num False Positives Distance Runtime Bonus 1 22 21 21 0 0 15.9311 71.91 1 2 22 21 23 0 2 13.6109 82.96 1 3 25 25 25 0 0 9.8625 80.48 0 4 22 22 24 0 2 11.3667 81.15 0 5 24 24 24 0 0 9.5960 69.59 0 6 23 23 23 0 0 11.5512 80.25 0 7 22 21 21 0 0 14.1537 71.52 1 Table of results for training images Approx. 95% accuracy with about 75 seconds runtime

    19. Training 1

    20. Training 2

    21. Training 3

    22. Training 4

    23. Training 5

    24. Training 6

    25. Training 7

    26. Conclusion • RGB Vector Quantization gave excellent segmentation • Morphological processing gave good estimate of centroids • Template matching with illumination correction gave near perfect results • Specific female was identified with headband

    27. Future Considerations • Edge detection to better separate the connected faces • Preprocess the image in HSV space before codebook comparison to improve runtime • Improve rejection of highly correlated non-face objects

    28. Thank You Questions ?