International Bulletin of Mathematical ResearchVolume 02, Issue 1, 2015

Pages 203-208, ISSN: 2394-7802

Face Detection Using Skin Color Model

Shalini Yadav1, Neeta Nain2 and Tapas Badal3

Department of Computer Science and Engineering,Malaviya National Institute of Technology, Jaipur-302017 (Rajasthan), INDIA.

Email: 1yadavshalini45@gmail.com, 2nnain.cse@mnit.ac.in, 3tapasbadal@gmail.com

Abstract

Face detection is a challenging task in image processing field. Now days face detection is used in many application which requiresidentification and verification. The major application areas of face detection are biometric field, crowd surveillance, photographyand many security related areas. Issue with face detection is that it should perform well in any condition such as illumination,color variance, brightness, pose variations. This paper proposed a technique for face detection based on skin color model. Forskin tone segmentation we are using YCbCr color model in algorithm. The reason behind using YCbCr is to remove illuminationcomponent which is represented by Y. We proposed a technique to detect face from image based on area and mark that region.The results of the algorithm is shown in figures. The proposed algorithm is tested on various dataset and gives better experimentalresults than traditional approaches.Keywords: Image Segmentation; Face Detection; Color Model; Median Filter

1 IntroductionFace detection technique is widely used in the area of biometrics, time tracking service, outdoor surveillance camera service,smart captcha, secured access, video chat service etc. The main purpose of face detection is for authentication. This approachis very useful in biometrics. The physiological methods are more stable than methods in behavioural category. The reasonbehind this is that physiological methods are not altered. Various methods are proposed for face detection technique some aretraditional and some are enhanced techniques. The problem with traditional technique is that it is affected in different lightningenvironment, identifying an individual from images of the face, so the result is erroneous. To differentiate the skin region andnon-skin region is a major task in face detection system. Face detection technique is improved if skin area and non skin areais measured. Skin classification decrease the misclassification rate that’s why this paper proposed a technique face detectionfollowed by skin classification. There are several new techniques and some enhanced techniques are proposed to overcome theseproblems. Several face detection approaches have been introduced [1]. Various methods are proposed for face detection i.e. theS-AdaBoost algorithm [2], Skin color based [3] [4], Neural Networks [5] [6], Bayes classifier [7], Chrominance based[8].In this paper we proposed color model based face detection approach. Proposed approach is simple as it uses color componentin RGB and YCbCr model to localize face region in an image and its computational complexity is low in comparison to otherexisting methods. This paper is organised as follows: Color model based face detection algorithm is described in Section 2, Ourmethodology is described in Section 3, Experimental results are discuss in Section 4, conclusion are given in Section 5.

2 Algorithm DescriptionThis section explains the basic steps used for face detection algorithm. Algorithm consist of following steps:A. Image retrievalB. Noise RemovalC. Skin ClassificationD. Face DetectionIn first step images are acquired from database or from live cameras. As face detection are perform in various situations whereimages contain noise it is required to perform noise removal technique on image. In our method we use median filter for noiseremoval. There are also some other filtration technique used in literature such as low pass filter and FFT but median filterperforms better for noise removal and smoothing the images.The framework of face detection algorithm used in this paper isshown in Figure 2.1.

204 Shalini Yadav, Neeta Nain, Tapas Badal

Figure 2.1: Framework of face detection algorithm

2.1 Image Retrieval

Images are taken from internet and some are from our database. To test proposed approach on images with different illuminationand lightning conditions we take images of different people with diverse background and also from different countries as shownin Figure 2.2.

Figure 2.2: Sample images used for testing.

2.2 Noise Removal

There are so many sources from which noise is produced in image such as when image is captured from camera then there is avariation of color and light environment. So to enhance the efficiency of algorithm we perform noise filter technique on images.Here we use median smoothing filter to remove noise and smoothing the image. For removal of noise one can use either low passfilter or FFT but the issue with this is that sometimes they also remove important information.

2.3 Skin Classification

Generally, face detection algorithm uses skin classification to enhance the efficiency of face detection algorithm[12]. To detecta face, area based technique is used and its efficiency is increased if skin is classified from images[13] [14]. Skin classificationapproaches are divided into three parts: parametric, non-parametric and skin cluster methods. Gaussian color distribution is usedin parametric approach [9] and skin color is estimated from histogram which is generated through testing images is used in non-parametric approach [10]. This paper use the third method which is widely used in skin classification field. This method use theapproach of thresholding for skin pixels. In skin detection technique, we technically transform the RGB color model into anothermodel and then classify skin by appropriate technique. There are so many color models ( i.e. IHLS,HSI,RGB, normalizedRGB(nRGB), Y CbCr and CIELAB) used for skin classification. This paper uses Y CbCr color model for skin classification

Face Detection Using Skin Color Model 205

[11]. Transformation of RGB to Y CbCr is given below in Equation 4.0.1.

(Y Cb Cr

)=(R G B

) 0.29 −0.168935 0.4998130.587 −0.331665 −0.4185310.114 0.50059 −0.081282

(2.3.1)

After changing the color model next task is to decide threshold for Y, Cb and Cr so it can classify skin correctly. In this colormodel a pixel is detected as skin if following condition is satisfy:

Y ≥ 180, 135 ≤ Cr ≤ 180, 85 ≤ Cb ≤ 135 (2.3.2)

An example of image segmentation using above color model is shown in Figure 2.3, where pixels with near to skin colorbecomes white while others are black.

Figure 2.3: Masked image after skin classification.

2.4 Face Detection

This section describes face detection and generate result as shown in Figure 2.4 and put the rectangle box on the faces. After skinclassification, the face detection algorithm gets more efficient. This algorithm performs area based calculation and find the averagevalue. This average value is use for thresholding to decide that whether the detected region is face or not. Algorithm is appliedon several images to train the system. Now this algorithm is applied on test images. This step takes input from the output of theskin classification step. Before finding the area belongs to face or not, there is a need to perform and morphological operations.During skin classification the eye part of human is set to 0 which indicates a black region so it splits the face in different partsi.e. one above the eyes and one below the eyes. Therefore to make a region into one area there is a need of connect neighbouringcomponents and filling holes. For this purpose we use imfill(), imopen() and imclose() morphological functions with differentstructuring elements. After completion of this, boundary box area is calculated. At this point we apply threshold on area. Ifselected area is greater than the threshold we marked the reason as face otherwise discarded.

3 Proposed Methodology

This section explains the methodology used in this paper. The algorithm used for face detection is described as follows:

Figure 2.4: Result image after face detection.

206 Shalini Yadav, Neeta Nain, Tapas Badal

Algorithm 1 Face Detection Algorithm.1.Read the input image file.2. Convert input RGB image into YCbCr image.3. Resize image into 256 X 256 size.4. Perform Noise removal using median filter.5. Initialize value Y as chrominance and Cb, Cr as luminance values respectively of image.6. IF(Y ≥ 180 & 85 ≤ Cb ≤ 135 & 135 ≤ Cr ≤ 180)

ThenIF(abs(Y − Cb) > 30 & Y > Cb & Y > Cr)ThenMark as skin regionElsereject that pixel

ENDIFENDIF7. Perform morphological processing with structuring element[3,3]8. Perform hole filling operation using imfill.9. Calculate labels for the connected objects in picture.10. Calculate average area for faces and set threshold value based on this value.11. FOR each image calculate bounding box area

IF (area is greater than threshold)Then

marked region as face and put rectangle box on that areaElserejected

ENDIFEND FOR

4 Experimental Result

This section describes different results obtained from system algorithm. From the results it can be easily shown that our algorithmgives better and faster result than traditional face detection algorithm. Our methodology can also detect face successfully withtilde faces or varying skin color. Algorithm also perform well in adverse lightning environment. As shown in Figure 4.1 on 207if human face is tilde then also system can detect faces successfully.

The face detection system was implemented using MATLAB on a 2.4 GHz Intel Core i3 machine running on 3 GB RAM.The accuracy of proposed methodology is checked on different datasets with single as well as multiple faces. The Table 1 depictsthe accuracy of our algorithm with single person images and Table 2 shows the resulting accuracy with multiple images which wehave proved experimentally. The accuracy of algorithm is computed as follows:

Accuracy =no. of correctly detected faces

total no. of images× 100 (4.0.1)

No. of images Positive Detection Accuracy100 96 96 %200 194 97 %

Table 1: Accuracy of the system for single person.

Face Detection Using Skin Color Model 207

No. of images Positive Detection Accuracy150 130 86.66 %

Table 2: Accuracy of the system for more than 1 person

Figure 4.1: Example images of face detection.

5 ConclusionThis paper proposed efficient and robust method for face detection that can perform better than traditional methods in variousadverse situations. First of all, color model is selected then skin classification is performed to enhance the efficiency of algorithm.After skin classification, face is detected based on area . Here the thresholding factor is area. Finally mark the region as faceif area is greater then the threshold value. This method gives the better result than feature based face detection algorithm. Thismethod can be used in many applications such as biometric attendance, identification, recognition, crowd surveillance and manyof security applications.

References[1] Lamiaa, Mostafa and Sherif Abdelazeem, ”Face Detection Based on Skin Color Using Neural Networks” GVIP 05 Confer-

ence, 19-21 December 2005, CICC, Cairo, Egypt.

[2] L. Sirovich andM. Kirby. ”Low-dimensional procedure for the characterization of human faces”. Journal of Optical Societyof America, 4(3):519–524, March 1987.

[3] Jiang Qiang-rong and Li Hua-lan,” Robust Human Face Detection in Complicated Color Images” IEEE Trans. on 2010.

[4] Randazzo Vincenzo, Usai Lisa, “An Improvement of AdaBoost for Face-Detection with Motion and Color Information” IEEE14th International Conference on Image Analysis and Processing (ICIAP) on 2007.

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[5] Anil K. Jain, Arun Ross and Salil Prabhakar,” An introduction to biometric recognition”, Circuits and Systems for VideoTechnology, IEEE Transactions on Volume 14, Issue 1, Jan. 2004 Page(s):4 – 20.

[6] Shih, P. and C. Liu (2004). ”Face Detection Using Discriminating Feature Analysis and Support Vector Machine in Video”,in: Proc. 17th Intl. Conf. Pattern Recognition, IEEE Computer Society Washington, DC, USA.

[7] Osuna, E., Freund, R. and Girosit, F. (1997). ”Training support vector machines: an application to face detection.” 130-136.

[8] Chandrappa D N, M Ravishankar and D R RameshBabe ”Automated Detection and Recognition of Face in a Crowded Scene”International Journal of Computer and Network Security, Volume 2, No. 6, June 2010, pp.65-70.

[9] Yang, M., Ahuja, N., 1999. ”Gaussian mixture model for human skin color and its application in image and video databases”.In: SPIE, pp. 458–466.

[10] Jones, M.J., Rehg, J.M., 2002. ”Statistical color models with application to skin detection”. IJCV 46, 81–96.

[11] Rehanullah Khan, Allan Hanbury, Julian Stottinger, Abdul Bais, 2012.”Color based skin classification”. 157-163.

[12] Pallabi Saikia, Gollo Janam, Margaret Kathing. (2012)”Face Detection using Skin Colour Model and distance between Eyes”International Journal of Computing, Communications and Networking, Volume 1, No.3, 2319-2720.

[13] Sayantan Thakur1, Sayantanu Paul1, Ankur Mondal2, Swagatam Das, Ajith Abraham . (2011)”Face Detection Using SkinTone Segmentation ”, World Congress on Information and Communication Technologies.

[14] R. Kjeldsen and J. Kender, “Finding Skin in Color Images,” Proc. Int. Conf. AFGR’06, Killington, Vermont, pp. 312-317,2006.

Shalini Yadav, is currently pursuing M.tech. at the department of Computer Science & Engineering, MalaviyaNational Institute of Technology, Jaipur. She has received her B.Tech. degree in Computer Science & Engineering from RajasthanTechnical University, Kota in 2013. Her research area is Face Detection and Recognition, and their applications.

Dr. Neeta Nain, is an assistant professor at the department of Computer Science & Engineering, Malaviya

National Institute of Technology, Jaipur. She was awarded her Ph.D. in Computer Science & Engineering from the MalaviyaNational Institute of Technology, Jaipur. She also received her MCA from Banasthali University. Her research area is Image pro-cessing, Pattern Recognition, Multimedia Techniques and Computer Graphics. Presently she is guiding research in HandwrittenText Recognition and Image vectorization.

Tapas Badal, is currently pursuing Ph.D. at the department of Computer Science & Engineering, Malaviya

National Institute of Technology, Jaipur. He received the B.Tech. degree in Computer Engineering from IES-IPS Academy Indorein 2006. He was awarded his M.Tech. in Computer Engineering from ABV - Indian Institute of Information Technology & Man-agement, Gwalior in 2009, which involved research into Advance Networks. His research area is Pattern Recognition, ActivityAnalysis, Content Based Video Processing.