Research Article Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier Rashed Mustafa, 1,2,3 Yang Min, 4 and Dingju Zhu 1,2,5 1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Department of Computer Science and Engineering, University of Chittagong, Chittagong 4331, Bangladesh 4 Department of Computer Science, he University of Hong Kong, Hong Kong 999077, Hong Kong 5 School of Computer Science, South China Normal University, Guangzhou 510631, China Correspondence should be addressed to Dingju Zhu; dj.zhu@siat.ac.cn Received 31 March 2014; Accepted 9 May 2014; Published 5 June 2014 Academic Editor: Yu-Bo Yuan Copyright © 2014 Rashed Mustafa et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Large exposure of skin area of an image is considered obscene. his 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. his 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 classiier and haar-like features used for ensuring detection accuracy. Skin ilter prior to detection made the system more robust. he experiment showed that, considering accuracy, haar-cascade classiier performs well, but in order to satisfy detection time, train-cascade classiier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. he detection rates for haar-cascade and train-cascade classiiers are 0.9875 and 0.8429, respectively. he detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classiier. 1. Introduction Online video and images are now easily accessible due to availability of high-speed Internet and rapid growth of multimedia technology. A report shows that a large number of teens and children search pornographic contents everyday [1]. his is a threat for the society and a concern of Internet safety. Taking care of this issue, scientists are working hard and initiated diferent ilter techniques to screen malicious contents. Most techniques were texts-based and could not identify objectionable materials from the sites appropriately. he reason for this is that there are countless websites which do not contain sensitive texts; hence, content-based image processing especially identifying obscenity has now been a challenging research area. It has been almost two decades when Forsyth et al. [2] published the irst paper in this issue on “Finding Naked People.” Ater that, a large number of works were accomplished by diferent researchers all around the globe [2–4]. he prior works concentrated mainly on skin color, which is not suitable because of skin-like objects and partially exposed images that are not considered obscene. In this paper we focused on nipple detection for iden- tifying objectionable images from pornographic sites. It is a challenging task because nipples are nonrigid objects varying in shape, size, scale, illumination, and partial occlusion [5]. he appearance also difers due to diferent ethnicity. Considering the above factors, in this research we extracted haar-like features from some cropped nipple images and used Gentle Adaboost (GAB) haar-cascade classiier for ensuring accuracy; in addition we have compared it with train-cascade classiier in order to satisfy detection time. It has been shown that haar-cascade classiier is suitable for accurately detecting nipples, but for ensuring faster detection and little accuracy train-cascade classiier is better. he rest of this paper can be organized according to the following ways: in Section 2 some related work will be discussed, some background knowledge including color model, haar-like features, and Gentle Adaboost algorithm Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 753860, 6 pages http://dx.doi.org/10.1155/2014/753860