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 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 • 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
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.
Results from initial quantization • Common problems identified
Better Code book developed • Problem areas broken up
Initial step of open and close performed to fill holes in faces • Elongated objects removed by check on aspect ratio and small areas discarded
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
Previous state stored to identify new regions when split occurs Superimposed mask image with eroded regions for estimate of centroids
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
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
Result from template matching and thresholding. Rejected - Red ‘x’. Detected Faces – Green ‘x’
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
Sample result of eigenface. Red ‘+’ is from morphological processing and green ‘O’ is from eigenfaces
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
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
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
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
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
Thank You Questions ?