This example shows how to track a person's face and hand using a color-based segmentation method.
The following figure shows the Color Segmentation example model:
To create an accurate color model for the example, many images containing skin color samples were processed to compute the mean (m) and covariance (C) of the Cb and Cr color channels. Using this color model, the Color Segmentation/Color Classifier subsystem classifies each pixel as either skin or nonskin by computing the square of the Mahalanobis distance and comparing it to a threshold. The equation for the Mahalanobis distance is shown below:
SquaredDistance(Cb,Cr) = (x-m)'*inv(C)*(x-m), where x=[Cb; Cr]
The result of this process is binary image, where pixel values equal to 1 indicate potential skin color locations.
The Color Segmentation/Filtering subsystem filters and performs morphological operations on each binary image, which creates the refined binary images shown in the Skin Region window.
The Color Segmentation/Region Filtering subsystem uses the Blob Analysis block and the Extract Face and Hand subsystem to determine the location of the person's face and hand in each binary image. The Display Results/Mark Image subsystem uses this location information to draw bounding boxes around these regions.