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 aprocess to classify the image containing and showing genital elements of human body from any kinds ofimages. 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 YCbCrskin segmentation and PCA, is employed to handle those variability problems. The function of skinsegmentation is to determine skin Region of Interest (ROI) image and skin probability. While the function of PCA is to extract eigenporn of the skin ROIs images and to project the skin ROI vector using the obtained eigenporns to holistic features. 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 experimentalresult shows that the proposed method can increase the accuracy by about 4.0% and decreases the FPR 20.6% of those of pornographic recognition using fusion descriptors, respectively. In addition, the proposed method is also robust for large size dataset that is shown by giving similar performance to the latest method (Multilayer-Perceptron and Neuro-Fuzzy (MP-NF)). The proposed method also works fast for recognition, which requires 0.12 seconds per image.
Keywords: pornographic, pca, image recognition, skin probability, and holistic features
Author: I Gede Pasek Suta Wijaya
Journal Code: jptkomputergg150103

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