EE368: Digital Image Processing Bernd Girod Leahy, p.1/15 Face Detection on Similar Color Images Scott Leahy EE368, Stanford University May 30, 2003.

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Presentation transcript:

EE368: Digital Image Processing Bernd Girod Leahy, p.1/15 Face Detection on Similar Color Images Scott Leahy EE368, Stanford University May 30, 2003

EE368: Digital Image Processing Bernd Girod Leahy, p.2/15 Problem Statement Goal: Find faces in an image –All images are in color –Images all contain a similar background –Images have a similar number of faces –Faces are all on approximately the same scale Design an algorithm which takes advantage of these facts

EE368: Digital Image Processing Bernd Girod Leahy, p.3/15 Sample Image

EE368: Digital Image Processing Bernd Girod Leahy, p.4/15 Detection Procedure Steps Involved: –Skin Detection –Morphological Processing –Template Matching –Face Coordinate Selection

EE368: Digital Image Processing Bernd Girod Leahy, p.5/15 Skin Detection Pixel by pixel, make a decision on the input based on the output –i = {skin, non-skin} –v = vector in color space (HSV, RGB, …) Treat the problem like a digital communications problem –Create a MAP Detector ? iv

EE368: Digital Image Processing Bernd Girod Leahy, p.6/15 Skin Detection (cont’d) MAP Detection –Minimize probability of error: Maximize p(i|v) over all inputs i –Often p(i|v) is not known, but: p(i|v) = p(v|i) * p(i) / p(v) (Bayes’ Rule) –p(v|i) and p(i) are more often known in a system

EE368: Digital Image Processing Bernd Girod Leahy, p.7/15 Histograms

EE368: Digital Image Processing Bernd Girod Leahy, p.8/15 2 Dimensional PDF Used only Hue and Saturation for MAP detector

EE368: Digital Image Processing Bernd Girod Leahy, p.9/15 3 Dimensional PDF Used all 3 coordinates for MAP detector

EE368: Digital Image Processing Bernd Girod Leahy, p.10/15 “Closing” Step Pseudo-Closing Step: –Dilation –Filling –Erosion

EE368: Digital Image Processing Bernd Girod Leahy, p.11/15 Template Matching Template matching involves convolving the image with some template –The average of the image being tested must be subtracted to eliminate biasing toward brighter areas Only one template used due to similar size and shape of faces in all images

EE368: Digital Image Processing Bernd Girod Leahy, p.12/15 Trial Templates Tried 4 templates, tweaking threshold until the best results were obtained

EE368: Digital Image Processing Bernd Girod Leahy, p.13/15 Face Selection Labeled all regions Selected only regions with areas bigger than some threshold Found the centers of the remaining regions and returned those as the results of the algorithm

EE368: Digital Image Processing Bernd Girod Leahy, p.14/15 Results

EE368: Digital Image Processing Bernd Girod Leahy, p.15/15 Conclusions Skin Detection and Closing –Takes advantage of images being in color –Takes advantage of similar statistics in the images Template Matching and Face Selection –Takes advantage of similar size and shape to faces Result: ~85% success rate

EE368: Digital Image Processing Bernd Girod Leahy, p.16/15

EE368: Digital Image Processing Bernd Girod Leahy, p.17/15

EE368: Digital Image Processing Bernd Girod Leahy, p.18/15