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.
Author: I Gede Pasek Suta
Wijaya
Journal Code: jptkomputergg150103
