FACE DETECTION, RECOGNITION AND TRACKING FROM VIDEOS

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1 International Journal of Recent Innovation in Engineering and Research Publication Impact Factor: by I2OR e-issn: FACE DETECTION, RECOGNITION AND TRACKING FROM VIDEOS M.Jahnavi 1 and M. Jasmine Pemeena Priyadarsini 2 1,2 School of Electronics Engineering, VIT University Abstract - Recognition and detection of face is of greater importance as they have many applications in real environiment and used in most of the security surveillance systems. It is easier to detect in still images but complex from videos. This can be rectified when tracking is done for the detected face continuosly in a video across frames. This paper presents effective method to track using trained features by Kanade Lucas Tomasi algorithm and to detect a face using Haar features by Voila Jones Algorithm Keywords - Haar features, Voila Jones, Kanade Lucas Tomasi(KLT) I. INTRODUCTION Face detection and tracking is a wide area of research due to its important applications in the image processing and analysing sectors as they are used in normal to military surveillance systems, public places, human robotic interactions and in various safety administrations where face tracking is used to analyze human emotional features which can ensure in prevent of accidents at hazardous machinery sites or driving when the driver is sleepy. Face detection is easier in a still image but complex in live videos due to various reasons like variation of poses, appearance and disapperance of faces, change in background illuminations and angle of view. Therefore tracking is employed to recognise the face in a video. Over the deacdes many algorithms are developed to detect and track a face. This paper presents simplest and effective way to detect and track multiple human faces using Voila Jones algorithm which uses Haar features for detection and Kanade Lucas Tomasi algorithm based on Haaris detector. II. FACE DETECTION Recognizing a human face in a real environment is difficult as it contains many non-faces also. It is important to detect a human face as it paves way for crucial applications such as video surveillance and safety measures. Voila Jones Algorithm Despite other algorithms present now Voila and Jones algorithm offers fastest detection and greater accuracy. It has four different stages for feature extraction which are Haar features, Integral images, Adaboost and Cascading. Paula and Michael Jones introduced this algorithm in 2001 and henceforth it became famous for its promising results. A. This Haar Features is a very simple straight forward method to extract and train features to the system. Haar features are simple rectangular features. The different types of Haar features are shown in Fig.1. The rectangles consist of black and white areas. The area in the haar features over here is the pixel gray value. Each Haar feature represents different features of the face which dominantly are eyes, nose, mouth and ears.fig.1(a,c.e) haar features are known as 2 rectangular features and fig.1(b) is a 3 rectangular and (d) is 4rectangular rights Reserved Page 45

2 Fig.1Different Haar features The haar features are applied over the face and as each haar feature represents different part of the face the relevant feature of the face matches with that particular haar feature. The black region represents the presence of a feature and the value is taken as +1 and the white region as -1. There is a threshold value for each feature. The difference between average sum of the pixels in the black area and the average sum of pixels from the white area is calculated. The resulting value should be equal or greater than the threshold value. Only then it is considered as a feature relevant to the face. Fig.2 Haar features representing face In a simpler way for example fig.1(b) represents the features of nose. The next stage of face detection is the use of integral image. B. Integral Image Summation of pixels in the rectangular area is a time consuming process. Hence integral images are used for simplicity and faster computation of the rectangular features. The integral image of a desired region is calculated by summing the pixel values above the area and to the left. Fig.3 shows the calculation of integral image of region D. Fig.3 Calculation of integral Image The integral image value at 1 is the pixel sum of A, at 2 the integral image value is the pixel sum of (A+B), at 3 the value is the pixel sum of (A+C) and at 4 it is (A+B+C+D). Therefore the integral image of D is (4+1)-(2+3). C. Adaboost Adaboost algorithm is the next stage of face detection. It separates non-faces from faces in a video frame. By combining weak classifiers adaboost forms the strong classifiers. Initially equal weights are given for all the features and weighted error is calculated. It is updated for every step so that more weight is given to the non-faces and less weight to the faces. Threshold value is used to compare the weights. Finally after the elimination of non-faces by comparision with threshold value only features relevant features with less weight remain. To reduce the computational time cascading Available Online at: 46

3 classifiers are utilized so that the non-faces are discarded immediately without any delay that is quite useful in real time applications. D. Cascading Cascading is a multistage process which works based on the information provided by the Adaboost on strong and weak classifiers and programmed to discard the non-faces. The entire frame is divided into numerous sub windows. Each sub window is searched for positive features i.e faces and negative features i.e non-faces. Initially a sub window is sent as input and cascading starts as stages. If any of the feature is found to be negative the sub window is immediately discarded else if found positive it further sent into next stage. If the sub window is found to have positive features in all the stages then only it is considered to be a face, if not it is discarded instantly. Then next sub window is taken as input and the above process is repeated until all sub windows are cascaded. Below is the flow chart on Cascading process Fig.4 Cascading flowchart Cascading process reduces the computational time which is very apt for real time applications. In simpler way to explain the above flow chart after the input is given it is searched for face if not immediately discarded at stage 1 itself thus saving time if not it proceeds to stage 2 and so on II. FACE TRACKING Tracking helps in keeping the trace of a object in a particular video though its in motion. Here the objective is to track the face. Recognition of faces is easier if the tracking of face is done across frames in a video. Kanade Lucas Tomasi Algorithm(KLT) This algorithm was developed by Lucas-Kanade initially and later developed by Tomasi- Kanade. Despite being more than 2 decades old this still is widely used as it enjoys considerable advantage being able to track faces in a automatic way. Multiple faces can be tracked in a video frame.the texture should be considerable for the features to be tracked. KLT algorithm is adept as the parameters in it reduce the disparity measurement of feature points of that of true translational model. Initially in this algorithm the displacement of the tracked feature points is computed from one frame to another. For example if the head tilts then all the tracked points also tilt with the head thus creating displacement. If the created displacement is calculated the position of head can be detected. Similarly all the tracked points move along with the head. Optical flow tracker tracks the features of the human face. The displacement of traceable features is thus utilized by KLT to track the human faces across frames in a video that can be either live or recorded video. Available Online at: 47

4 Fig.5 Flow chart of KLT Algorithm 2-D translation model is used in this paper. At the start harris corners are detected for a face in the initial frame. Now using optical flow tracker the feature points are calculated across frames by computation of displacement. Motion vectors are connected between successive frames to get the harris points and track them. For every 10 to 15 frames as a precaution not to lose the track of harris points the harris detector is applied. It also ensures in tracking of new and old harris points. Fig.6 2-D translation motion of frames Consider initial point as (x,y). In the next instant displacement occurs by a variable (b1, bn). Thus the value of new displaced point shall be equal to the sum of initial point and the vector of displacement. Hence the new coordinates are x =x+b1 and y=y+b2. Warp function is used here now to estimate the displacement which is W(x;p) = (x+b1 ; y+b2). B. Estimation of displacement The initial frame from which the harris points were detected is considered to be the template image.at later stages the tracking points are taken by computing the difference between current displacement and preceding points. The displacement is calculated as following: 2 x I W x; p T x (1) where displacement parameter is p Assuming that initial value of parameter p is known now finding p 2 x [I W x; p + p)) T(x)] (2) Using Taylor series approximation and differentiating with respect to p T. T x I W x; p (3) p = H 1 x I w p H is the hessian matrix. Hence displacement p is found and the next traceable can be found III. RESULTS Available Online at: 48

5 MATLAB software is used for implementation of the process. Either live video using webcam or a recorded video in the avi format can be given as input. Fig.7 Snapshot 1 from a recorded video Fig.8 Snapshot 2 from a recorded video The Snapshot 1 and 2 are taken from a running recorded video that was provided as input to the MATLAB. Now using live webcam as input it starts detecting and tracking faces. Available Online at: 49 Fig.9 Snapshot from webcam Based on the the simulation of results. We can conclude that the results are promising. IV. CONCLUSION For this process Voila and Jones algorithm is used to detect the face and Kanade Lucas Tomasi algorithm to track the detected faces. This method greatly reduces the computation time and

6 improves accuracy of results. This is very much useful in video surveillance and safety applications. In future work a particular face is necessary to be detected and recognized neglecting other faces. This applies in tracking of a particular object also. REFERENCES [1] Ragini choudhury verma, cordelia schmid, and krystian mikolajczyk face detection and tracking on a video by propagating detection probabilities IEEE TRANSACTIONS INTELLIGENCE, VOL.25, NO.10, OCTOBER [2] Dorin comaniciu and visvanathan Ramesh, robust detection and tracking of human faces wit an active camera, IEEE visual surveillance [3] Chistian ku blbeck and Andreas Ernst, Face detection and tracking in video sequences using the modified census transformation, Electronic Imaging Department, Fraunhofer Institute for interated circuits, Hsin-chu Germany, [4] MAMATA S.KALAS, REAL TIME FACE DETECTON AND TRACKING USING OPENCV, international journal of soft computing and Artificial Intelligence, May [5] Nandita sethi and alankrita Aggarwal, robust Face Detection and tracking using pyramidal Lucas kanade tracker algorithm, IJCTA, vol. 2, October [6] ZhuLiu and yao wang, FACE DETECTION AND TRACKING IN VIDEO USING DYNAMIC PROGRAMMING IN VIDEO USING DYNAMIC PROGRAMMING, Department of Electrical engineering polytechnic university, Brooklyn video sequences, pulsar team, INRIA. [7] Etienne corvee and francois Bremond, Combining face detection and people tracking in video sequences, Pulsar team, INRIA. [8] Ragini choudhury verma, cordelia schmid and krystian mikolajczyk, face detection and tracking a video by propagating detection probabilities, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL.25,NO.10,October [9] Dmitry mikhaylov, anton samoylov, peterminin and Alexey egorov face detection and tracking from image and statistics gathering, IEEE conference on signal-image technology and internet-based systems [10] Divya George and arunkant a.jose, FACE DETECTION AND TRACKING AT DIFFERENT ANGLES IN VIDEO USING OPTICAL FLOW,ARPN journal of engineering and applied sciences, vol.10 No.17, September [11] Peter gejgus and martin sperka, :face tracking for expressions simulations, International conference on computer systems and technologies, [12] Paul viola and Michael jones,:rapid object detection using a boosted cascade of simple features, CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION,2001. [13] L. stan and Z. Zhang, floatboost lerning and statistical face detection, IEEE trans. on pattern analysis and machine intelligence. vol. 26, No. 9, [14] [15] Available Online at: 50