A few years back, one of the feature extraction methods implemented in vRate was a Nudity Detection algorithm designed to compute the probability of nudity in an image frame. Primary design goal for this method was to speed up the real-time processing for vRate’s Media Analysis Webservice queries, while maintaining a high accuracy rate, and minimizing false negatives.
For scenarios where accuracy is of primary concern, template-matching is a robust alternative to other faster approaches such as simple skin detection. To demo this algorithm, the team released a vRateLite© solution that implemented an intermediate method (vRate_Nudity_Detection_Lite) that introduced simple heuristics to compute a probability by aggregating skin percentage values for blobs detected in the foreground of the image frame. This eliminated requirements for lengthy comparisons between the target image and a library of templates with favorable processing speeds.
Next generation of vRate Nudity Detection algorithms incorporated template-matching to handle frames with high risk of skin exposure as well as grayscale or monochromatic images.