Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier

Abstract EN

Large exposure of skin area of an image is considered obscene.

This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts.

This paper presents a novel method for detecting nipples from pornographic image contents.

Nipple is considered as an erotogenic organ to identify pornographic contents from images.

In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy.

Skin filter prior to detection made the system more robust.

The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable.

To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images.

The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively.

The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier.