Pornographic Image Recognition Based on Skin Probability and Eigenporn of Skin ROIs Images

Abstract

The paper proposed a pornographic image recognition using skin probability and principle component analysis (PCA) on YCbCr color space. The pornographic image recognition is defined as a process to classify the image containing and showing genital elements of human body from any kinds of images. This process is hard to be performed because the images have large variability due to poses, lighting, and background variations. The skin probability and holistic feature, which is extracted by YCbCr skin segmentation and PCA, is employed to handle those variability problems. The function of skin segmentation is to determine skin ROI image and skin probability. While the function of PCA is to extract eigenporn of the skin ROIs images and by using the eigenporns the holistic features are determined. The main aim of this research is to optimize the accuracy and false rejection rate of the skin probability and fusion descriptor based recognition system. The experimental result shows that the proposed method can increase the accuracy by about 12% and decrease the FPR and FNR by about 16%, respectively. The proposed method also works fast for recognition, which requires 1.3.second per image.