Two Phases Neural Network-Based System for Pornographic Image Classification

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1 SETIT th International Conference: Sciences Of Electronic, Technologies Of Information and Telecommunications March 2226, 2009 TUNISIA Two Phases Neural NetworkBased System for Pornographic Image Classification Usama SAYED *, Samy SADEK ** and Bernd MICHAELIS *** * Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt usama@aun.edu.eg ** Mathematics & Computer Science Department, Faculty of Science, Sohag University, Sohag, Egypt samy.sakr@yahoo.com *** Institute for Electronics, Signal Processing and Communications,University of Magdeburg, Magdeburg, Germany Bernd. michaelis@ovgu.de Abstract: A robust model for skin detection is the primary need of many fields of computer vision, including face detection, gesture recognition, and pornography detection. In 1996, the first paper on pornographic image detection was published. Since then, different researchers argue different color spaces to be the best choice for skin detection in pornography detection. Unfortunately, no comprehensive work attempts to use more than one color space and evaluate its performance for detecting pornography. In this paper, a new two phases neuralbased system for classifying images into two classes pornographic and nonpornographic is proposed. The proposed system makes use of a fast and precise neural network model based on adaptive transfer function, called Multilevel Sigmoidal Neural Network (MUSNN). Furthermore, the system exploits 5 color spaces in all their possible representations for skin detection in pornographic images. Receiver Operating Characteristics (ROC) curve of the proposed system shows that our system outperforms other pornography detection schemes in the context of detection rate and false positive rate. Plenty of experimental results are presented including photographs and a ROC curve calculated over a test set of photographs, which show stimulating performance. Keywords: Multilevel Sigmoidal Neural Network, Pornography, Color space, Contentbased retrieval INTRODUCTION Images are a vital part of today s World Wide Web. The statistics of more than 4 million HTML webpages reveal that 70.1% of webpages contain images and that on average there are about 18.8 images per HTML webpage [STA 02]. On the other hand, images are also contributing to harmful (e.g. pornographic) or even illegal (e.g. paedophilic) Internet content. So effective filtering of images is important in an Internet filtering solution. To block pornographic content some representative companies as Net Nanny and SurfWatch operate by maintaining lists of URL s and newsgroups and require constant manual updating. Abundant literature is available, but the Internet is very rapidly evolving, not only quantitatively. Each day, 3 million pages are appearing on the Web. Detection based on image content analysis has the advantage to process equally all the images without the need for frequent updating, so will produce more effective filtering. When dealing with detection of pornography in color images, it is important to have a good algorithm of recognizing skin regions. Much work has been done in this regard. References [ANG 01] [HSU 01] [KRU 02] [SHI 02] [STÖ 99] [YAN 98] [BRE 02] discuss detection of human skin and the effect of different cameras, lightsetting, human race and color spaces on the recognition process. Furthermore, references [FOR 96] [REH 99] also use texture information as a component in the skin detection. As a third component in object recognition, many researchers have looked at the shape of the object as a last stage in the information gathering process [ALS 96] [COH 97] [JAI 96] [PAR 01] [SAF 00] [ZHE 06]. Veltkamp and 1

2 Hagedoorn [VEL 99] have written a survey of different stateoftheart shape matching methods. With a set of features describing the image, such as color and shape for segmented objects, it is possible to build fully system for pornographic images detection. The proposed approach is as follows. The first main phase is skin pixels classification. We build a neural networkbased model for the skin distribution. This model uses more than one color spaces. Several simple features from image pixels are calculated. The features are then fed into Multilevel Sigmoidal Neural Network (MUSNN) classifier. The output of this phase is a grayscale skin map with the gray levels being proportional to the skin probabilities. The second main phase is pattern recognition. Several features are calculated from the skin map and ellipses are fitted which form a pattern. A MUSNN classifier is then trained on the training set patterns here; the output of this phase is a real p [0,1] value, probability. proportional to the pornography The rest of this paper is organized as follows: Section 2 discusses the multilevel transfer functions as a functional extension to the existing feedforward neural model. In section 3 a neuralbased skin pixels classifier that makes use of Ohta, Normalized RGB, and YCrCb color spaces is presented. Section 4 is devoted to shape feature extraction and pattern recognition. In section 5, some experimental results are presented. Section 6 concludes this paper. 1 sgd ( x) = (1) 1 βx + e Where, β is the steepness factor of the function. The multilevel form of the sigmoidal function is derived from the previous standard form as fallow: MUSGD( x) sgd( x) + ( λ 1) sgd( c), ( λ 1) c x < λ c (2) Where, λ represents the color index and 1 λ N, N is the number of color scale objects or levels. Here, c represents the color scale contribution. Multilevel sigmoidal (MUSGD) transfer functions for three and five levels (N) are depicted in figures 2 and 3. Figure 1. Sigmoidal transfer function 1. Multilevel transfer functions The neuralbased image classifier has the advantage of being fast (highly parallel), easily trainable and capable of creating arbitrary partitions of feature space. However a neural model, in the standard form, is sometimes unable to correctly classify images. This is due to the fact that each of the component single neuron employs the standard bilevel transfer function as the characteristic transfer function. Since the bilevel transfer function produces only binary responses, the components of the neural network can generate only binary color outputs. So, in order to produce multiple color responses, a functional extension to the existing neural model is required. A multilevel transfer function is a functional extension of the standard transfer functions in existence [BHA 07]. Several multilevel forms pertaining to several standard transfer functions can be designed. At this point, we discuss the basic design mechanism of the multilevel versions of the standard sigmoid (MUSGD) transfer function as follow. The standard sigmoidal transfer function (see figure 1) is given by: 2 Figure 2. MUSGD transfer function for N=3 Figure 3. MUSGD transfer function for N=5 2. Skin pixels classification In this section, a neural networkbased skin pixels classifier that uses Ohta, Normalized RGB and YCrCb color spaces has been described Skin classifier in Ohta space The color axes of Ohta space [OHT 80] are the 3 largest eigenvectors of the RGB space, found

3 through principal components analysis of a large selection of natural images. This yields: I1 = I2 I3 R + G = R B, + = R 2G + B, B (3) The advantage of the Ohta color space is that the color channels are approximately decorrelated, which makes it a good choice for computing color features Skin classifier using RGB channels ratio It was observed, that pixels belonging to skin region regularly contains a significant level of red. Using this observation, certain values of the two g = G/ R and b = B/ R ratios can be used as skin presence indicators [BRA 00] Skin classifier in normalized RGB space Normalized RGB is a representation that is easily obtained from the RGB values by a simple normalization procedure: 2.5. Skin pixels classifier Since there are too many outside factors such as lighting that can change the apparent color of skin, and of course, different people have different colored skin. In addition, objects in the background may be the same color as a person's skin and there is no clear way of telling the difference. To address the above problem, the proposed classifier uses a set of color spaces to extract 9 features for each pixel. It is stated that using pixel features from more than one color spaces is a good idea to precisely extract a more defined skin region. In the implementation of the proposed skin pixels classifier there are some main steps viz. (1)apply neuralbased pixels filter to identify the skin regions, (2)apply median filter to get rid of impulsive saltpepper noise, (3)apply morphological open/close operations to connect broken regions, and (4)The skin regions of areas smaller than predefined threshold,θ are deleted. These regions are too small to be counted as human skin. The main steps of the proposed skin classifier are shown in figure 4. (4) RGB image NN classifier As the sum of the three normalized components is known (r+g+b = 1), the third component does not hold any significant information and can be omitted, reducing the space dimensionality. The reason for using this color space is due to evidences that the human skin color is more compactly represented in it than it is in other color spaces, such as RGB, HSI, SCT and YQQ [PEE 03] [AHL 99] [LIT 97]. Here the two chromaticities r and g are used to describe the skin color Skin classifier in YCrCb space YCrCb is an encoded nonlinear RGB signal, commonly used by European television studios and for image compression work. Color is represented by luma (which is luminance, computed from nonlinear RGB, constructed as a weighted sum of the RGB values, and two color difference values Cr and Cb that are formed by subtracting luma from RGB red and blue components. Y = 0.299R G B, Cr = R Y, Cb = B Y (5) The transformation simplicity and explicit separation of luminance and chrominance components makes this color space attractive for skin color modeling [MEN 00] [YAN 98] [SAB 98]. Skin map Figure 4. Main steps of skin pixels classifier 3. Pornography detection Median filter Open/close filter Small skin regions removal One interesting application of skin pixel classification is as part of a larger system for detecting pornography in photos. A pornography detector that worked reliably to detect pornographic images could be a valuable tool for image search services in digital libraries [WAN 98], as well as for image categorization. The main goal of the proposed pornographic image classification system is to determine whether or not an input image contains a pornographic content by feeding the output of the skin classifier to an MLP classifier, which outputs a number between 0 and 1, with 1 for pornographic image and 0 for nonpornographic image. Figure 5 shows an overview of the proposed pornographic image classification system. 3

4 source image blocked image NN2 NN1 first phase second phase skin map Figure 5. An overview of pornographic image classification system Shape features extraction Many of the features used here are based on the fit ellipses calculated on the skin map, since they could meet the requirement for simplicity and capture some important shape information. It is observed from experiments that for approaches based on skin detection, portraits have a tendence to be classified as pornographic images since generally portraits expose plenty of skin as pornographic ones. The fit ellipses will hopefully at least help discriminate portraits from pornographic images. The features extracted from the skin map includes: average skin probability of the whole image, height and width of the largest region of skin, average skin probability inside the largest skin region, number of skin regions in the image, distance from the centroid of the largest skin region to the center of the image, edge of connected components of skin, percentage of pixels detected as skin, and number of connected components of skin Pornography recognition post processing images shape feature extraction The Multilevel Sigmoidal Neural Network (MUSNN) classifier used here is a semi linear multilayer feedforward network with two hidden layers. The basic model of the MUSNN is most similar to the classical network structures but with improving in the hiddenunit adaptive transfer function. Simulation results demonstrate that the performance of the MSNN in classification accuracies is significant and a computational time is faster than the standard sigmoidal NN models. The net outputs a number between 0 and 1, with 1 for pornographic image and 0 for not. The learning procedure starts off with a random set of weight values. For each training pattern p, the net evaluates the output yp in a feedforward manner. To decrease the error between the output yp and the true target tp, the net calculates the corrections of the weight 4 values using the backpropagation procedure. This procedure is repeated for all the patterns in the training set to yield the resulting corrections for all the weights for that one iteration. In a successful learning exercise, the system error will decrease with the number of iterations, and the procedure will converge to a stable set of weights, which will exhibit only small fluctuations in value as further learning is attempted. In the test phase, for each test pattern, the net calculates the output in one pass. A threshold T, 0<T<1 is then set, to get the binary decision. 4. Experimental results Due to the lack of any standard image databases for testing and comparison of pornographic image classification systems, the proposed system has been tested using our own images database. This database has 667 test pornographic images and 1580 assorted control images, containing some images of people but none of pornographic images. All images were taken of type RGB format with (nominal) 8 bits / pixels in each color channel. The test images were collected from the internet. They show a very wide range of postures. Some depict several people including naked or scantilydressed. Some depict only small parts of the bodies of one or more people. Most people in the images are Caucasian; a small number are Blacks or Asians. To evaluate the results of the proposed pornographic image classifier two different metrics are used. TP (true positive) is the number of pornographic images identified correctly divided by the number of all test images. FP (false positive) is the number of nonpornographic images identified as pornographic images divided by the number of all control images. Receiver operating characteristics (ROC) curve that shows the relationship between correct classification and false classification of the proposed pornographic image classifier as functions of a predefined threshold, T is shown in figure TP Figure 6. ROC curve of the proposed pornographic image classifier. Figure 7 shows the capability of the proposed FP

5 classifier system to classify some clothed images in the test set. These images correctly classified as containing pornography content. (i) (ii) 5. Discussion and comparison of results This paper has shown that the pornographic images can be precisely classified using a combination of simple visual cuescolor, and human figure characteristics. The proposed system successfully classifies 89.9% of the test images, but only 3.3% of the control images. Arentz and Olstad [ARE 04] have proposed a method for helping to identify adult web sites by using the imagecontent as means of detecting erotic material. The average image classification error rates for offensive sites were 14.1%, as compared to 9.8% for nonoffensive. Table 1 summarizes the performance of [ARE 04] system and the proposed one. The figures in the table show that the proposed system improves the performance over the system developed by Arentz and Olstad in the context of classification rate and false positive rate. Table 1. Performance comparison for two pornographic images classifiers. Classifier TP FP (iii) P=0.981 p=0.998 p=0.999 p=0.975 Figure 7. Typical test images correctly classified as pornographic images: (i) Source images; (ii) Result of first phase; (iii) Result of second phase. Mistakes by the proposed pornography classifier occur for several reasons. In some images, the pornographic content is too small to detect. In others, most or all of the skin area is nonsaturated, so it fails the skin classifier. Some control images pass the skin classifier because they contain people, particularly several closeup portrait shots. Other control images contain material whose color closely resembles that of human skin; particularly wood, sand, and skin or fur of certain animals. Fig.8 shows some control images that the proposed system may fail to classify since they take a skinlike color and their average pornography probabilities are very high. p=0.552 p= p=0.563 p=0.791 Figure 8. Typical control images wrongly classified as pornographic images Arentz et al. classifier 85.9% 9.8% The proposed classifier 89.9% 3.3% REFERENCES [AHL 99] Ahlberg, J., A system for face localization and facial feature extraction, Tech. Rep. LiTHISYR 2172, Linkoping University, [ALS 96] Alshuth P., Hermes T., Klauck C., Kreyss J., and Roper M., Iris image retrieval for images and videos, =pdf [ANG 01] Angelopoulou E., Understanding the color of human skin, In Proc. SPIE Conf. On Human Vision and Electronic Imaging VI (SPIE) 2001, SPIE Vol. 4299, SPIE Press, pp , May [ARE 04] Arentz W.A. and Olstad B., Classifying offensive sites based on image content,computer Vision and Image Understanding, 94: , [BHA 07] Bhattacharyya S., Dutta P., Maulik U. and Nandi P. K., "Multilevel Activation Functions for True Color Image Segmentation Using a Self Supervised Parallel Self Organizing Neural Network (PSONN) Architecture: A Comparative Study", International Journal of Computer Science, vol 2, no. 1, pp. 0921, [BRA 00] Brand J., and Mason J., A comparative assessment of three approaches to pixellevel human skindetection, in: Proc. of the International Conference on Pattern Recognition, 2000, vol. 1, pp

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