Automatic nipple detection based on face detection and ideal proportion female using random forest

Abstract

Currently pornographic image on the online world, teenagers and kids can visit easily. Which stimulate sexual desire. Resulting behavior of sexual abuse, enticing a child under the age of 15 years increased, cause problems getting pregnant and sexually transmitted diseases. Pornographic detection is essential to prevent to access through analyzing image content. Many researchers are interested in pornographic detection of nipple using extended Haar-like for extracting the features, color, texture and shape that are used for classification using various algorithms cascaded AdaBoost. However, this disadvantage is the templates for nipple which require a lot of training set and it consumes the time to detect a multiple possible position similar to nipples such as eyes and navel. This research proposed the novel algorithm without using templates for detecting the nipple. Our proposed creates the novel model based on ideal proportion detection. The result of this algorithm shows the high accuracy and reducing the computational time when compares with the existing method.