Vol. 4, No. 1 Jan 2013 ISSN Journal of Emerging Trends in Computing and Information Sciences CIS Journal. All rights reserved.

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1 An Automatic Face Detection and Gender Classification from Color Images using Support Vector Machine 1 Md. Hafizur Rahman, 2 Suman Chowdhury, 3 Md. Abul Bashar 1, 2, 3 Department of Electrical & Electronic Engineering, International University of Business Agriculture and Technology, Dhaka-1230, Bangladesh ABSTRACT This paper presents combined face detection and gender classification method of discriminating between faces of men and women. This is done by detecting the human face area in image given and detecting facial features based on the measurements in pixels. The proposed algorithm converts the RGB image into the YCbCr color space to detect the skin regions from the facial image. But in order to detect facial features the color image is converted into gray scale image. This paper presents appearance-based approach with Gabor filter and Support Vector Machine (SVM) classifier. Gabor filter banks are used to extract important facial features, SVM classifier is then used to recognize the facial features. It is proved that SVM can provide superior performance. Different kernel functions have been useful in cases where the data are not linearly separable. These kernel functions transform data to higher dimensional space where they can be separated easily. Keywords: Face detection, Gender classification, SVM, Gabor filter. 1. INTRODUCTION Human s face is a prominent feature in machine learning and computer vision system. A face conveys various information including gender, age, ethnicity etc. Face information is applicable in many sectors like biometric authentication and intelligent human-computer interface. Since our main concern of this paper is gender classification from human faces so a proper localization of human face area is necessary. For both face detection and gender classification purpose selection of color space for detecting skin region is a main concern. Extracting two sets of data for both male and female and separate them accurately is a challenging job. So we need to select a better classifier to improve the classification performance. Among all kinds of recognition algorithms, support vector machine (SVM) is one of the most popular classification methods, providing a sound theoretic basis for constructing classification models with high generalization ability. The earliest attempt to use computer vision techniques for gender classification was based on neural networks [14]. Golomb et al [15] trained a fully connected two-layer network, called SEXNET, to identify gender from facial images. Tamura et al [15] used a multi layered neural network to identify gender from face images. Different techniques have been introduced recently, for example, Matta et al. [1] combined temporal and spatial information such as head motion, mouth motion, and facial appearance to perform gender classification. Moghaddam and Yang [2] developed the first automatic system for combined face detection and gender classification. They used maximum-likelihood estimation for face detection and for facial feature detection. For gender classification, they used several different classifiers. The experiments were carried out with a set of FERET images [3] achieved accuracies as high as 96.6%. Shakhnarovich, Viola, and Moghaddam [4] applied AdaBoost to the features used by the face detection system created by (Viola and Jones, 2001) on pixel images collected by crawling the web. They obtained an accuracy of 79%. Castrillon-Santana and Vuong [17] compared the performance of humans and automatic face recognition algorithms for gender classification. The automatic face recognition algorithm used Principal Component Analysis (PCA) and SVM for feature extraction and classification. In [5], S. Buchala proposed a gender classification system based on a SVM classifier and features obtained using PCA (Principal Component Analysis), CCA (Curvilinear Component Analysis) and SOM (Self Organizing Maps). Best results are obtained using 759 PCA components, 7.75% overall error rate, but the size of the faces is restricted to 128x128 pixels and the test set is composed by only 80 faces. Gutta, Wechsler, and Phillips [6] applied a hybrid system of RBFs and decision trees to FERET images [3] at a resolution of pixels, and achieved an accuracy of 96%. Baluja and Rowley [7] presented an Adaboost system for gender classification with manually aligned faces. They carried out a thorough experimental comparison between the Adaboost and an SVM classifier by varying face image scaling, translation, and rotation. Similar comparative analysis was conducted. Moghaddam [8] proposed a gender classification system based on the use of SVM classifier. The employed features are the pixel elements themselves. The obtained results are very good, 3.38% overall error rate when using a RBF kernel, but the test set consists of only 259 faces. Using Laplace and Gabor filters, Scalzo et al. [18] proposed an evolutionary genetic learning algorithm based framework to unify feature fusion and decision fusion. The performance of the algorithm was computed on a database of 400 frontal images and the results showed an error rate of 3.8%. Most of the aforementioned methods have achieved impressive performance on controlled databases such as FERET [3]. However most of these algorithms use manual detection and alignment of the face images which has been shown to improve performance [9]. Only few studies have automatically extracted faces using a face detector [4], [6], [8]. One of the challenges of automatic 5

2 gender classification is to account for the effects of pose, illumination and background clutter. Practical systems have to be robust enough to take these issues into consideration. Most of the work in gender classification assumes that the frontal views of faces, which are pre-aligned and free of distracting background clutters, are available. In this paper we propose a model for automatic face detection and gender classification with Gabor filter for feature extraction. The features extracted from the training set are used for training an SVM classifier. This paper is structured as follows. In section 2, related methodology for face detection and gender classification is described, in section 3 experimental results are shown and section 4 contains conclusion. 2. METHODOLOGY Our proposed automatic face detection and gender classification method is described in Figure 1. Testing Image images. Several computer vision approaches have been developed for skin detection. The apparent difference in skin color perceived is mainly due to the darkness or fairness of the skin, characterized by the difference in the brightness of the color, which is governed by Y but not Cb and Cr in YCbCr color space. Y, luminance component is brightness component, whereas Cb and Cr are chrominance components, which correspond to color components. In the color detection process, each pixel is classified as either skin or non-skin based on its color components. In the skin color detection process, each pixel was classified as skin or non-skin based on its color components. The detection window for skin color was determined based on the mean and standard deviation of Cb and Cr component, obtained using 85 training faces in 10 input images. The Cb and Cr components of 85 faces are plotted in the color space in Figure 2. Pre-processing Feature Extraction Train Classifier Testing Image Face Detector Model Pre-processing Feature Extraction Output Fig 1: General Approach for Gender Classification system A. Face Detection The approach on this paper will use mainly the color based algorithm with the technique of color space transformation from RGB (red, green and blue) to YCbCr (luminance, chrominance blue and red). The proposed method first detects the face region using skin-color from image. The given input RGB image is converted into the YCbCr color space using following equations. Y = 0.299R G B (1) Cb = R 0.331G B (2) Cr = 0.500R 0.419G 0.082B (3) Skin detection is the process of finding skincolored pixels and regions in an image or a video. This process is typically used as a preprocessing step to find regions that potentially have human faces and limbs in Fig 2: Skin pixel in YCbCr color space As noted above, skin color of individuals will fall in a small area of color space. This threshold can be done very simply on a component or on a combination of several components. 90<Y<180, 90<Cr<130, 80<Cb<150 This range may vary for people of different ethnicities. We can consider two or more threshold range for better result. Input image is then converted to binary image. To remove small areas that have been obtained in previous stage, geometric operations, using the available filters, will be done on this area. These processes (Dilation, Erosion, and Hole filling) remove many of unacceptable areas from the face area. The final output after segmentation of skin area for example is shown in Figure 3. 6

3 the image, stands for the number of non-black pixels in the image. By calculating the average of the maximum and minimum channel percentage, an adaptive mean gray value of the whole image is gained. Figure 4 illustrates some examples of images and the result images after applying the LC algorithm. Fig 3: (a) Original image. (b) Skin area segmentation B. Preprocessing The problem of elimination of non-standard illumination is one of the most complicated problems in the area of computer vision, due to the complex illuminated environment in the real world. In face detection and gender recognition problems, non-standard illumination effects become severe. The accuracy on detecting skin color in complex background is difficult to increase. It is because the appearance of skin-tone color depends on lighting condition. In the past, many researches assume that chrominance is independent to luminance. However, in practice, skin tone color is nonlinearly dependent on luminance. The technique of lighting compensation uses top 5% of luma (nonlinear gamma-corrected luminance) as reference white and re-adjusts the chrominance value in each pixel if the value of luma is too high or too low. The main usage of this technique is to remove yellow bias color. According to [10], the lighting compensation (LC) algorithm is very efficient in enhancing and restoring the natural colors into the images which are taken in darker and varying lighting conditions. Therefore, lighting compensation has been used in their skin and face detection algorithms, and they stated that this algorithm is indispensable for robust skin-tone color detection. The LC algorithm can be defined as followings: Fig 4: Image before and after lighting compensation C. Feature Extraction Since face recognition is not a difficult task for human beings, selection of biologically motivated Gabor filters is well suited to this problem. A 2D form of Gabor wavelet [11] consists of a planer sinusoid multiplied by a two dimensional Gaussian is used for image processing. 2D Gabor wavelet highlights and extracts local features from an image, and it has the tolerance of changes in location, shape, scale and light. Here is the formula of Gabor wavelet in space domain: ( ) [ ( ) ] (4) (8) (5) The formula in frequency domain is defined as follows: { [ ]} (9) (6) (7) [ ] The Gabor wavelet transform adopted in our system is: (10) (11) Where, stands for the scale factor for one specific channel of R, G or B. The and separately stand for the standard mean gray value of the specific channel and the mean value non-black pixels in the same channel. Here stands for the number of pixels in (12) Represents a pixel in the image, scale is a parameter of spatial frequency, is an orientation angle. 7

4 (13) Where, k is the number of orientations. This wavelet can be used at 8 orientations ( n= 0,... 7 ) and 5 spatial frequencies ( scale= 1,,5 ). An image is converted into 40 images with 5 scales and 8 orientations and the features are the individual Gabor filters coefficients. The operation is very complex and slow in spatial domain, so we use FFT in frequency domain and then IFFT to obtain the output in spatial domain. Figure 5 shows example of Gabor Filter with five scales and eight orientations and Figure 6 shows how single image is convolved with Gabor Filter of five scales and eight orientations. input is mapped to high-dimensional feature space where they can be separated by a hyper plane. This projection into high-dimensional feature space is efficiently performed by using kernels. More precisely, given a set of training samples and the corresponding decision values, { }, the SVM aims to find the best separating hyper plane given by the equation that maximizes the distance between the two classes. The main task in training SVM is to solve the following quadratic problem: Subjected to- ( ) (14) D. Feature Vector Generation There are different ways to form the feature vector for training the classifier. Some of them even use whole image as a feature vector and perform classification which needs high computation. So here feature vector is made from important values of the image from each filter Energy, mean and standard deviation forming a 40 value feature vector for every image. (15) Where, C is the penalty parameter and K is kernel function. In this case, the problem can be equivalently understood in terms of projecting the input data into a higher dimensional space where they are separated using to parallel hyper planes. There exist many popular kernel functions that have been widely used for classification. The polynomial kernel function is of the form- ( ) (16) And the radial basis function is given by- ( ) (17) Fig 5: Gabor Filter with five scales and eight orientations Fig 6: The Gabor features of single face image E. Support Vector Machine The SVM [19] is a learning algorithm for classification. It tries to find the optimal separating hyper plane such that the expected classification error for unseen patterns is minimized. For linearly non-separable data the By using different kernel functions, SVM can implement a wide variety of learning algorithms. It is well known that the SVM has a great potential to perform well. However, the performance of the SVM is very closely tied to the choice of the optimal kernel functions. There has been a lot of research over the last few years on algorithms to help choose the exact type of kernel for a given problem with a certain set of features. Most of these methods are based on simple heuristics based on the knowledge of the input data and there has not been any standardized method to obtain the best kernel. Hence, the choice of the optimal kernel has reduced to a trial and error procedure in most scenarios. 3. EXPERIMENTAL RESULTS To evaluate the performance of gender classification algorithm, we have prepared a database by combining images from several existing databases with different ethnicity and nationalities and referred as the mixed database. Since the focus of this research is gender recognition, we have selected frontal images with slight expression and illumination variations. It contains a total of 995 faces which includes 515 male and 480 female faces. These are the faces used for training. The male faces are indexed as +1, while the female faces as -1. 8

5 Table I provides the composition of the database. It contains images from the CMU PIE [12], AR [13], FERET [3] face databases. The comparison results of different algorithms tested in [9] and our methods are shown in Table II. We also change the high and low frequency of the Gabor filter bank and do some experiments, the results are shown in Table III. We have designed GUI using MATLAB-2008a which is shown in Figure 8. Some sample output results are shown in Figure 9. TABLE 3: GENDER CLASSIFICATION RESULTS USING DIFFERENT FREQUENCIES Frequency Male Female 0.1~ % 78.4% 0.1~ % 87.75% Fig 7: Some output from face detector which is used in this paper TABLE 1: DETAILS OF THE MIXED DATABASE Database No. of male No. of female face images face images CMU PIE [12] AR [13] Indian Face Chinese Face FERET [3] Total (995) Fig 8: GUI panel for Gender Classification TABLE 2: COMPARISON RESULTS OF DIFFERENT ALGORITHMS Methods Male Detection Rate (%) Female Detection Rate (%) Neural Network Threshold Adaboost LUT Adaboost Mean Adaboost LSVM SVM+Pol SVM+RBF Fig 9: Some output results of gender classification 9

6 4. CONCLUSION In this paper, we have presented a color model conversion algorithm based on chrominance color information is used for face region detection. By applying the threshold measurements in pixel, face area is detected from color image. This achieves a great deal of accuracy. Output of the face detector is cropped and used as input to the gender classifier. We have presented SVM as classifier in which the SVM classifier is learned by Gabor features. We tested our approach on random images from internet, picture taken by digital camera and achieved great deal of accuracy about 88%. Conventional gender classification methods can just detect the gender of the given face image, most of them can not automatically detect face from the image. But our proposed method can automatically detect the face area within the color image and detect the gender of the face. The experimental results reveal that the proposed method is much better in terms of altered circumstance. This method can be used in video surveillance system, forensic applications, secure access control, solving face recognition problems and so on. Finally, we conclude that there is still room for improvement in gender classification methods. REFERENCES [1] F. Matta, U. Saeed, C. Mallauran, and J.- L.Dugelay, Facialgender recognition using multiple sources of visual information. In Proceedings of Workshop on Multimedia Signal Processing, pp , [2] B. Moghaddam and M.H. Yang, Gender Classification with Support Vector Machines, Proc. Int l Conf. Automatic Face and Gesture Recognition, pp , Mar [3] P.J. Phillips, H. Wechsler, J. Huang, and P.J. Rauss, The FERET Database and Evaluation Procedure for Face Recognition Algorithms, Image and Vision Computing J., vol. 16, no. 5, pp , [4] Shakhnarovich, Gregory, Viola, Paul A., and Moghaddam, Baback. A Unified Learning Framework for Real Time Face Detection and Classification. Int. Conf. on Automatic Face and Gesture Recognition, [5] S. Buchala, N. Davey, R. J. Frank, T.M. Gale, M. Loomes, W. Kanargard, Gender Classification of Face Images: The Role of Global and Feature- Based Information, ICONIP 2004, Calcutta, India, Lecture Notes in Computer Science 3316, pp [6] Gutta, S., Wechsler H., and Phillips, P. J. Gender and ethnic classification. IEEE Int. Workshop on Automatic Face and Gesture Recognition, pages , [7] S. Baluja and H.A. Rowley, Boosting Sex Identification Performance, Int l J. Computer Vision, vol. 71, no. 1, pp , [8] B. Moghaddam, M.-H. Yang, Learning Gender with Support Faces, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 24, No. 5, pp , [9] E.Makinen and R. Raisamo, An experimental comparison of gender classification methods, Pattern Recognition Letters, vol. 29, no. 10, pp , [10] Chen, P. and Grecos, C. (2005), A Fast Skin Region Detector, ESC DIVISION RESEARCH 2005, Department of EEE, Loughborough University, pp [11] M. Lyons, J. Budynek, A. Plante, S. Akamatsu, Classifying Facial Attributes using A 2-d Gabor Wavelet Representation and Discriminant Analysis, Proceedings of the 4th International conference on Automatic Face and Gesture Recognition, 2000, pp [12] T. Sim, S. Baker, and M. Bsat. The CMU pose, illumination, and expression (PIE) database of human faces. Technical Report CMU-RI-TR-01-02, Robotics Institute, January [13] A. M. Martinez and R. Benavente. The AR face database.cvc Technical Report #24, [14] G. Cottrell, J. Metcalfe, and E. Face, Gender and Emotion Recognition using Holons, [15] B. Golomb, D. Lawrence and T. Sejnowski, Sexnet: a neural network identifies sex from human faces, Advances in Neural Information Processing Systems, pp , [16] S. H. Tamura, Kawai, and H. Mitsumoto, Male/Female Identification from 8 x 6 Very Low Resolution Face Images by Neural Network, Pattern Recognition, vol. 29, no. 2, pp , [17] M. Castrillon-Santana and Q. C. Vuong, An analysis of automatic gender classification. In Proceedings of Conferenceon Progress in Pattern Recognition, Image Analysis and Applications, pp , [18] F. Scalzo, G. Bebis, M. Nicolescu, and L. Loss, Feature fusion hierarchies for gender classification. In Proceedingsof International Conference on Pattern Recognition,

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