With the steady growth of the amount of images publicly available on the Internet

adult image recognition is of great significance for ensuring web security and content monitoring.In this paper

single-person adult pictures are studied. Current skin-based adult image recognition algorithms usually have a high false positive rate. Here

an adult image recognition algorithm

which takes the image of an adult torso as the region of interest (ROI)

is proposed to reduce false-positives efficiently. The proposed algorithm utilizes Poselet to detect the ROIs with plenty of discriminative information. Each Poselet provides examples for training a linear SVM (support vector machine)classifierthat can be run over the image in a multiscale scanning mode

after which the outputs of these Poseletdetectors vote for the localization of the torso and other body parts. Then

based on the ROIs

discriminative Fisher vectors for nude breast image classification are obtained. However

owing to variations in human body appearance

the ground truth positions and the outputs of the torso detectormay shift. An adaptive algorithm is proposed to overcome such a weakness. The algorithm selects several torso candidate areas according to the confidence values obtained by the torso detector; the algorithm then integrates the discrimination results of several areas to obtain the final result. To train the SVM classifier based on torso detection

a set of 30 000 pornographic images was collected

and the pornographic regions in the images are manually labeled. The labeling information can be used to generate the training data automatically. To evaluate the method

a new and large dataset is built

which includes adult

benign

and bikini images. Experiments on this dataset reveal that the proposed method obtains an accuracy rate of 91.7%

which is much higher than the traditional skin color-based method. The Poselet-based torso detection method obtains more pornography-related information compared with other methods. Thus

the proposed method can detect adult images with a high detection rate and a low false positive rate

making it suitable for practical applications.