An Integration of Face detection and Tracking for Video As Well As Images

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1 An Integration of Face detection and Tracking for Video As Well As Images Manish Trivedi 1 Ruchi Chaurasia 2 Abstract- The objective of this paper is to evaluate various face detection and recognition methods provide information for image based face detection and recognition as an initial step for video surveillance. In this paper we are going to explain some important fact of face detection which are very much useful in many applications like face recognition, facial expression recognition, face tracking, facial feature extraction, gender classification, identification system, document control and access control, clustering, biometric science, Human Computer Interaction (HCI) system, digital cosmetics and many more. An enormous number of face recognition techniques have been developed in last few decades. In this paper an attempt is made to review a wide range of methods used for face detection and face recognition comprehensively. Index Terms- Face detection, face recognition, video, Eigen values 1. Introduction The face is our elemental focus of attention in social life playing an interesting role in conveying identities and emotions. We can detect and recognize a number of faces learned throughout our duration and identify faces at a glance. Now a days, Face detection is used in many places especially the websites hosting images like photofunia, picassa, photobucket and facebook etc. The automatic tagging is a beautiful feature which adds a new dimension to sharing pictures among the people who are in the picture and also make the idea to folk about who the person is in the particular image. Face detection is perturbation with finding whether or not there are any faces present in a given image (usually in gray scale) and if any image is present, take back the image location and content of each face. This would be the first step of any fully automatic system that examine the information contained in faces (e.g., identity, gender, expression, age, race and pose). The block diagram of face detection as shown in fig. [1] While recent work covered mainly with upright frontal faces. On the other hand various systems have been developed that are able to detect faces fairly accurately with in-plane or outof-plane rotations in real time. With the help of video stream we can improve the performance of face detection of single image. Earlier, face recognition has pull in much attention and its research has quickly prolonged by not only engineers but also neuroscientists. As I said Face detection is a silent feature of face recognition as the first step of Automatic Face Recognition. However, face detection method has lots of variations of image appearance, such as pose variation which include front and non-front, closure, image orientation, enlightening condition and facial expressions. Various recent methods have been proposed to resolve for detection. For example, the template-matching methods [1], [2] are used for face localization and detection by computing the correlation of an input image to a standard face pattern. The feature invariant approaches are used for feature detection [3], [4] of eyes, mouth, ears, nose, etc. The appearancebased methods are used for face detection with eigenface [5], [6], [7], neural network [8], [9], and information theoretical approach [10], [11]. However, implementing these methods altogether is

2 still a challenging task. Fortunately, the images which are used in this research have some amount of degree of uniformity thus the detection algorithm can be simpler:- i) the all the faces are vertical and have frontal view; ii) they are under almost the same illuminate condition. This paper presents a face detection technique mainly based on the color segmentation, image segmentation and template matching methods. Block diagram of face detection fig. [1] 2. Methodology 2.1 Color Segment Detection of skin color in color images is a very popular and useful method for detection of faces. Numerous techniques [12], [13] have reported for locating skin color regions in the input image. While the input color image is typically in the RGB format, these techniques usually use color components in the color space. This is because RGB components are subject to the lighting conditions hence the face detection can be fail if the lighting condition changes. In the YCbCr color space, the luminance information is contained in Y component; and, the chrominance information is in Cb and Cr. Therefore, the luminance information can be easily de-embedded. The RGB components were converted to the YCbCr components using the following formula. Y = 0.299R G B Cb = R G B Cr = 0.500R G B In the skin color detection process, each pixel was classified as skin or non-skin based on its color components. 2.2 Image Segmentation Image segment is the next step for detection.image segment is to separate the image fleck in the color filtered binary image into individual regions. This process will happened with the help of following three methods. i) This step is to fill up black isolated holes and to remove white isolated regions which are smaller than the minimum face area in training images. The threshold (according of image) is set conservatively. The filtered image followed by initial erosion only leaves the white regions with reasonable. ii) This step is used to separate some integrated regions into individual faces, the Roberts Cross Edge detection algorithm is used. 2.3 Facial feature detection This method involves estimation of area of the facial features, like as lip & mouth determination, right and left eye locating, cheek and chin identification and noise estimation process. The middle part of the image is represented as Xmid and Ymid respectively and these can be calculated as- Xmid = width/2 Ymid = height/2 Then all the pixels that lie between the region of (Ymid (height of image/5) 10) and (Ymid + height of image/5) (Xmid-(width of image/5) and

3 (Xmid + (width of image/5) constitutes the facial feature area enclosing the eyes, nose, mouth. The lip area is calculated by searching a point at the left bottom and then move slowly towards in upward direction. The scan is done column wise and the searching will be stop as the pixel will found. Height of the mouth area is from (detected lip point 15 row pixels) to (detected lip point + 15 row pixels) and width of the mouth area from (detected lip point + 45 column pixels). Similarly, a point is found that might be the left most pixel of the right eye and from that detected point, the width and height of the right eye is estimated. Similarly left eye portion is found. The width of the nose area is calculated as the difference between the right eye s end point and the left eye s starting point. The height of the nose is estimated by the region that starts at the right eye s endpoint and ends at the detected lip point plus 25 row pixels [14]. 3.3 Gender classification Support Vector Machines are based on the concept of decision planes that clarify decision boundaries. A decision plane is one that distinct between a set of objects having different class memberships. The proposed method uses the linear SVM. For all the image pixel values SVM [index] = Sum of intensity value of 3 layers (RGB) of the pixel in the image No. of pixels so far processed Where 255 is the maximum intensity value of the gray level intensity and 3 is the total no. of layers. The value in the array SVM[index] is given as the input to the function LEGENDRE( ) that returns the value of the associated Legendre polynomial Pm(x) where x is the value evaluated in this expression. Its range should be between -1 & +1. L is the scaled integer array, L >0 which specifies the order of the function. If it specifies, then it is the linear SVM classification If the resultant SVM is greater than the estimated threshold value 0.07, then the face in the given input image is of male otherwise it is of female. 3.4 lower plane masking To remove the possibility of false texture which is originating from this we have to remove the lower portion if the image and remaining pixels will me remain for face detection. This lower plane masking can be done by two methods: first one is Morphological processing and second one is Removal of blobs and gray scale. 4. ALGORITHM FOR THE FACE DETECTION 1. Image is resized by using of filtering method and sub sampling and the region is selected to be pixels. 2. Convert into gray scale image with the template and normalize the output by the energy in the template. 3. Compare peak in the result of output and given range of threshold. 4. for prevention of false detection marked those pixels whose fall in threshold region.. 5. The threshold range is reduced to present lower limit and then another stage of convolving is applied. If the lower limit is reached then proceed to the next level. 6. To detection of larger scale faces, template should be enlarged and thresholds are reset to the upper limit and again the whole process is carried out.

4 7. Finally As the upper stage is reached, end the process. We will get that there are the peaks at the location of faces and these peaks are closely related to each other [15]. 5. Result REFRENCES [1] I. Craw, D. Tock, and A. Bennett, Finding face features, Proc.of 2nd European Conf. Computer Vision. pp , [2] A. Lanitis, C. J. Taylor, and T. F. Cootes, An automatic face identification system using flexible appearance models, Image and Vision Computing, vol.13, no.5, pp , [3] T. K. Leung, M. C. Burl, and P. Perona, Finding faces in cluttered scenes using random labeled graph matching, Proc. 5th IEEE int l Conf. Computer Vision, pp , Face detection of real image based collage For color image Fig [2] [4] B. Moghaddam and A. Pentland, Probabilistic visual learning for object recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no.7. pp , July, [5] M. Turk and A. Pentland, Eigenfaces for recognition, J. of Cognitive Neuroscience, vol.3, no. 1, pp , [6] M. Kirby and L. Sirovich, Application of the Karhunen-Loeve procedure for the characterization of human faces, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.12, no.1, pp , Jan Page 18 [7] I. T. Jolliffe, Principal component analysis, New York: Springer-Verlag, [8] T, Agui, Y. Kokubo, H. Nagashi, and T. Nagao, Extraction of face recognition from monochromatic photographs using neural networks, Proc. 2nd Int l Conf. Automation, Robotics, and Computer Vision, vol.1, pp , Face detection of real image based collage For gray scale image Fig [3] 6. Result 1. Interrogation. 2. Photography 3. Marketing [9] O. Bernier, M. Collobert, R. Feraud, V. Lemaried, J. E. Viallet, and D. Collobert, MULTRAK: A system for automatic multiperson localization and tracking in real-time, Proc, IEEE. Int l Conf. Image Processing, pp , [10] A. J. Colmenarez and T. S. Huang, Face detection with information-based maximum discrimination, Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp , [11] M. S. Lew, Information theoretic view-based and modular face detection, Proc. 2nd Int l Conf.

5 Automatic Face and Gesture Recognition, pp , [12] H. Martin Hunke, Locating and tracking of human faces with neural network, Master s thesis, University of Karlsruhe, [13] Henry A. Rowley, Shumeet Baluja, and Takeo Kanade. Neural network based face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(I), pp.23-38, [14] S.Ravi, S.Wilson, Face detection with facial features and gender classification based on support vector machine, 2010 IEEE International Conference on Computational Intelligence and computing Research, ISBN: [15] Michael Padilla and Zihong Fan, EE368 Digital Image Processing Project- Automatic Face Detection using Color Based Segmentation and Template/Energy Thresholding, Department of Electrical engineering, EE368, Stanford University.