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Eye tracking and detection by using fuzzy template matching and parameter based judgment
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Eye tracking and detection by using fuzzy template matching and parameter based judgment
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1. INTERNATIONALComputer VolumeOF COMPUTER ENGINEERING International Journal of Engineering and Technology (IJCET), ISSN 0976- JOURNAL 4, Issue 1, January- February (2013), © IAEME 6367(Print), ISSN 0976 – 6375(Online) & TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 4, Issue 1, January- February (2013), pp. 80-88 IJCET© IAEME: www.iaeme.com/ijcet.aspJournal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEMEwww.jifactor.com EYE TRACKING AND DETECTION BY USING FUZZY TEMPLATE MATCHING AND PARAMETER BASED JUDGMENT 1 TARUN DHAR DIWAN, 2UPASANA SINHA ASSISTANT PROFESSOR DEPT. OF ENGINEERIN 1 Dr.C.V.RAMAN UNIVERSITY, BILASPUR (INDIA) 2 J K INSTITUTE OF ENGINEERING, BILASPUR (INDIA) 1 email@example.com, firstname.lastname@example.org ABSTRACT The eyes are tracked and correlation scores between the actual eye and the corresponding “closed-eye” template are used to detect blinks. In which a fuzzy template is constructed based on the piecewise boundary. A judgment of eye or non eye is made according to the similarity between input image and eye template. Eye blinking is one of the prominent areas to solve many real world problems. The work that has been carried out for eye tracking only is not suitable for eye blink detection. Stored template for a particular depth is chosen. Once the template is chosen and the system is in operation, the subject will be restricted to be at the specified distance. Another disadvantage of the system is that changing camera Positions require the whole system to be retrained the process of blink detection consists of two phases. These are eye tracking followed by detection of blink. The work that has been carried out for eye tracking only is not suitable for eye blink detection. Therefore some approaches had been proposed for eye tracking along with eyes blink detection. This paper implements one of the approaches given by Michael et al . Further more the result of template creation accuracy and total blink detection to count number of eye blinks in an image sequence. Online template is completely independent of any past templates that may have been created during the run of the system. Keywords - template, frames, Interface, testing, automatically, involuntary. 1. INTRODUCTION There has been a growing interest in the field of facial expression recognition especially in the last two decades. The primary contribution of this research is automatically 80
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEinitializing the eye blink detection in an image sequence for real time eye tracking applications. Thenever ending saga of traffic accidents all over the world are due to deterioration of driver’s vigilancelevel. Drivers with a depleting vigilance level suffer from a marked decline in their perception;recognition and vehicle control abilities & therefore pose a serious danger to their own lives and thelives of the other people. For this reason, developing systems that actively monitors the driver’s levelof vigilance and alerting the driver of any insecure driving condition is essential for accidentprevention . Many efforts have been reported in the literature for developing an active safetysystem for reducing the number of automobiles accidents due to reduced vigilance. Though advancesafety features are provided such as advances in vehicle design, including the provision of seat beltsand airbags and improvements in crashworthiness have led to considerable casualty reductions inrecent years .However, future increases in road traffic will. Make it difficult to meet future casualtyreduction targets unless more advanced accident avoidance technologies can be introduced.2. RELATED WORK Lots of works have been carried out to detect face and extract features from it. Main facialmuscles that correspond to facial changes are eyebrow raiser, eyebrow frowning, lip suck and eyeblink . Whenever we talk about eye blinking, tracking of eyes become built in need. Lots ofapproaches have been developed to track eyes. Kanade et al.  proposed a method to locate eyes instatic images which was improved & re implemented several times. Kanade et al.  have shown thatthe approaches used for eye tracking cause error in case of eye blinking if it is incorporated into animage sequence. Therefore, Farhan et al.  proposed eye tracker along with blink detectionalgorithm. Here they first detect the face by using motion based head segmentation. For tracking theeye, they used the inner corner of eyes as invariant property because this property is invariant towardsthe lighting changes. Variance map of frames of image sequence and statistical operation onconnected components were used to detect the eye blink. It also used normalized correlationcoefficient to detect eye blink. This coefficient is insensitive to lighting condition so it gives betterresult.3. METHODS The algorithm used by the system for counting the eye blinking in the video taken by USBcamera is initialized automatically, dependent only upon the inevitability of the involuntary blinkingof the user. Motion analysis techniques are used in this stage, followed by online creation of atemplate of the open eye to be used for the subsequent tracking and template matching that is carriedout at each frame. [2, 8, 9]A flow chart depicting the main stages of the system is shown in Figure 1. Flow chart of the Approach of eye blinks detection. 81
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME The first step in counting the blinking of the user is to locate the eyes. For this, the difference of two subsequent frames is taken and then thresholding is done. The resulting image shows the regions of movement that occurred between the two frames. Next to remove the noise of background movement, an Opening morphological operation is performed by using diamond shape structuring element. The reflection of light on the surface of glasses makes the diarized eyes small pieces of disconnected areas, so we used eyebrows as the Primary feature to locate sunglasses . The eyebrow is one of visible and stable features of the face, so it can be used as a secondary feature in sunglasses detection. The accurate position of eyebrows will determine the accuracy of detecting sunglasses. To locate eyebrow as one separate region, the main difficulty is that when the driver turns aside, the area representing eye or eyebrow may be connected with dark parts surrounding the head, which makes it impossible to search such an area. So, we need to split the desired connected region apart from its surrounding [11, 12]. Figure2. Gray image and resulting binary image We try to locate the region that is likely to contain primary features (such as eyes, eyebrows)based on Then connected components in the resultant image is found and labeled. Fordiscarding the other movement except eye blinking, filtering of unlikely eye pair is done whichis based on the computation of six parameters for each component pair: the width and height ofeach of the two components and the horizontal and vertical distance between the centroids ofthe two components [13, 14]. Thus after this process, eye pair is received if present otherwisesteps are continued for other subsequent frames. Figure 3. Shows the images of the output of above process.After locating the eye pair, a template of 55x55 size of one of the eye is created. For detectingthe eye blink, normalized correlation function is used in each frame of the video which givesthe value between 0 and 1. Then the maximum value of the correlation coefficient is takenfrom each frame. If its value is greater than 0.94 then eye is considered open otherwise close.So the counting of close eye frames is done to count the number of times the eye is blinked. 82
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME4. TEMPLATE CREATION If the previous stage results in a pair of components that passes the set of filters, then it is agood indication that the user’s eyes have been successfully located. At this point, the location of thelarger of the two components is chosen for creation of the template. Since the size of the template thatis to be created is directly proportional to the size of the chosen component, the larger one is chosenfor the purpose of having more brightness information, which will result in more accurate trackingand correlation scores. Figure 4. Open eye templatesSince the system will be tracking the user’s open eye, it would be a mistake to create the template atthe instant that the eye was located, since the user was blinking at this moment. Thus, once the eye isbelieved to be located, a timer is triggered. After a small number of frames elapse, which is judged tobe the approximate time needed for the user’s eye to become open again after an involuntary blink,the template of the user’s open eye is created [1, 3, 16]. Therefore, during initialization, the user isassumed to be blinking at a normal rate of one involuntary blink every few moments. Again, nooffline templates are necessary and the creation of this online template is completely independent ofany past templates that may have been created during the run of the system.5. ExperimentsTable 1 Results of Template Generation Accuracy, Automatic Blink Detection, Manually BlinkDetection, Missed Blink Detection. Automatic Blink Detection Manually Blinks Detection Missed Blink detection 167 151 31 184 167 24 181 156 25 139 123 9 176 170 24 Total Total total 847 767 113 83
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEME6. RESULT AND DISSCUSATION Table 2 Results of Template Generation Accuracy, Accuracy of Blink Detection. Template Generation Accuracy Find out Error Accuracy of Template matching 75 % 10.59% 89.41 % 80 % 10.18% 89.82 % 80 % 16.02% 83.98 % 75 % 13.008% 86.99 % 85 % 3.52% 96.47 % Total Total Total 395 53.318% 446.67% Avg. Avg. Avg. 79% 10.66% 89.33%For experiment, total 100 videos are used in different lightning condition using inbuilt USBcamera of Samsung RV 509. The size of each frame is 480x640. The result of templatecreation accuracy and total blink detection is tabulated in Table 1 for each video.847automatic template creation is achieved and 79% accuracy and 89.33% accuracy of templatedetection is achieved in counting of eye blink for 100 videos. The result may be tested formore number of videos. Figure 5. Template Generation 84
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMETemplate Generation:We have ploted a pia chart for template generation in which you cansee he percentage of tem1plate generation is ploted by different colour.As shown video 1to20 is navy blue representing 19% of template generation, the maroom colour represents videofrom 21 to 40 consisting of 20% present of template generation then comes video 41 to 61which is dark in colour an consists of template generation then from video 61 to 80 isreresented by violet colour consisting of 19% template generation and lastly from video 81 to100 consists of 22% of template generation and is represent by sky blu colour Figure 6. Accuracy of Template matchingfor more convience we have also ploted of graph shown template generation accuracy in thiswe have divided it into 6 bars in which 5 bars shown the video from 1to 100 and 6th bar showthe overall template generation from 1 to 20 the accuracy 75% from 21 to 40 the accracy is80% from video 41 to 60 it is 80% from video 61 to 80 it is 75% from the video81 to 100 it is85% and the over all template generation accuracy is 79%. Figure 7. Template DetectionTemplate Detection : We have plotted a pia chart for template detection in which you can seehe percentage of template detection is ploted by different colour.As shown video 1to 20 isnavy blue representing 89.41% of template detection, the maroom colour represents videofrom 21 to 40 consisting of 89.82% present of template detection then comes video 41 to 61consisting of 83.98% which is dark in colour an consists of template detection then from 85
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEvideo 61 to 80 is reresented by violet colour consisting of 86.99% template detection andlastly from video 81 to 100 consists of 96.47% of template detection and is represent by skyblu colour. Figure 8. Accuracy of Template Detectionfor more convince we have also plotted of graph shown template detection accuracy in thiswe have divided it into 6 bars in which 5 bars shown the video from 1to 100 and 6th bar showthe overall template detection from 1 to 20 the accuracy 89.41% from 21 to 40 the accracy is89.82% from video 41 to 60 it is 83.98% from video 61 to 80 it is 86.99% from the video81to 100 it is 96.47% and the over all template detection accuracy is 89%.7. CONCLUSION After studying and analyzing results of above technique following points isconcluded:1. A good accuracy is achieved in different illumination conditions. Testing must be done onlarge database of videos.2.over all template generation accuracy is 79% and template detection accuracy is 89%. 3. The initialization technique is efficient and gives good results. The system respondsslowly and requires more work for real time implementation.8. APPLICATION AREA Automobiles. Security Guard Cabins. Operators at nuclear power plants where continuous monitoring is necessary. Pilots of airplane. Military application where high intensity monitoring of soldier is needed. Medical sectors for Eye related problems. Personal identification system 86
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEREFERENCES Tarun Dhar Diwan "Real Time Eye Template generation system in an imagesequence",CIIT International Journal Of Digital Image Processing, June2012, ISSN: 0974-9586,DOI: DIP062012013. Tarun Dhar Diwan“Automatic Eye Blink Tracking & Detection in an Image Sequence"InternationalJournal of Computer Science and Information Technology, Vol- 2 , pages 2348-23492011, ISSN 0975 – 9646. Tarun Dhar Diwan"Improve Frame Generacy Accuracy With USB Cameras" InternationalJournal of Electronics and Computer science Engineering, Volume1, 2012, pages 1427-1432, ISSN2277-1956. Tarun Dhar Diwan "Eye Tracking and Detection by Using Template Generation and Parameter Based Judgment"CiiT International Journal Of Digital Image Processing, August 2012 ,ISSN: 0974-9586, DOI: DIP.082012006 Tarun Dhar Diwan "Local Binary Pattern Occuence Map Method for High Parallel ImageProcessing" International Conference on Advances in Computing and Communication Aprl 8-10,2011, pages 538-540, ISBN:978-81-920874-0-5, IEEE,NIT Hamirpur, Himachal Pradesh, India Tarun Dhar Diwan "personal identification system ", CiiT - International Journal of Data MiningKnowledge Engineering, June2012, and ISSN: 0974-9578, DOI: DMKE062012004. Y. Tian, T. Kanade & J. Cohn 1999. "Multi-State Based Facial Feature Tracking andDetection.Robotics Institute", Carnegie Mellon University, Technical Report CMU-RI-TR-99-18.. X. Xie, R. Sudhakar & H. Zhuang. On Improving Eye Feature Extraction Using DeformableTemplates. Pattern Recognition 27,pages 791-799,1994.. A. L. Yuille, D. S. Cohen & P. W. Hallinan. Feature Extraction from Faces Using DeformableTemplates. Proceedings Computer Vision and Pattern Recognition, pages 104-109, 2008. X. Wei, Z. Zhu, L. Yin, and Q. Ji. A real-time face tracking and animation system. Proceedingsof the CVPR Workshop on Face Processing in Video (FPIV2004),Washington, D.C., June 28 2004. M. Betke,W. Mullally, and J. Magee. Active detection of eye scleras in real time. Proceedings ofthe IEEE CVPR Workshop on Human Modeling, Analysis and Synthesis (HMAS 2000), Hilton HeadIsland, SC, June2000. T.N. Bhaskar, F.T. Keat, S. Ranganath, and Y.V.Venkatesh. Blink detection and eye tracking foreye localization. Proceedings of the Conference on Convergent Technologies for Asia-Pacific Region(TENCON2003), pages 821–824, Bangalore, Inda, October15-17 2003. S. Crampton and M. Betke. Counting fingers in real time:A webcam-based human-computerinterface with game applications. Proceedings of the Conference on Universal Access in Human-Computer Interaction (affiliated with HCI International 2003), pages1357–1361, Crete, Greece, June2003.. J. Deng and F. Lai." Region based template deformation and masking for eye feature extractionand description". Pattern Recognition, pages 403–419, 1997. D.O. Gorodnichy. On importance of nose for face tracking. Proceedings of the IEEEInternational Conference on Automatic Face and Gesture Recognition (FG 2002), pages 188–196,Washington, D.C., May 20-21 2002. “Digital Image Processing Using MATLAB” Rafael C. Gonzalez, Richard E.Woods,StevenL.Eddins, Mc Graw Hill, Second Edition, 2010. Mr.J.Rajarajan, Dr.G.Kalivarathan, “Influence Of Local Segmentation In The Context Of DigitalImage Processing – A Feasibility Study”, International journal of Computer Engineering &Technology (IJCET), Volume 3, Issue 3, 2012, pp. 340 - 347, Published by IAEME. 87
International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 1, January- February (2013), © IAEMEAUTHOR BIOGRAPHIESTarun Dhar Diwan received his Master of Engineering (Computer Technology andApplication) degree from Chhattisgarh swami Vivekananda technical university –Bhilai,India, and Master of Philosophy (Gold Medal list) from Dr. C.V. Raman University. He iscurrently HOD & Mtech Coordinate at the Dr.C.V.Raman University-bilaspur, India. HisCurrent research work artificial intelligent, Image Processing and Software Engineering. 88