Pupil Center Detection Using Edge and Circle Characteristic

Transcription

1 Vol.49 (ICSS 04), pp Pupil Center Detection Using Edge and Circle Characteristic Gung-Ju Lee, Seok-Woo Jang, and Ge-Young Kim, Dept. of Computer Science and Engineering, Soongsil Universit 369, Sangdo-Ro, Dongjak-Gu, Seoul , South Korea {lkj097, Dept. of Digital Media, Anang Universit, 37-Bungil, Samdeok-Ro, Manan-Gu, Anang , South Korea Abstract. This paper suggests an algorithm for detecting the pupil center from the input images obtained through the single camera in real time. The method suggested in this paper is to calculate the pupil center after finding the chord of pupil b using the fact that the vertical bisector of the chord in the circle passes the center of the circle. To achieve this goal, the initial pupil center is detected using VPF (Variance Projection Function) firstl. The accurate pupil central point is then etracted b detecting the circle based on the detected central point. In eperimental results, it is shown that the suggested pupil detection method is ecellent in terms of the detection rate and processing time. Kewords: Edge, Circle Characteristic, Pupil, Vertical Bisector, Threshold. Introduction As the information communication technolog is developed, the studies on HCI (Human Computer Interface) have increased. Among those, the interface sstem based on the ee movements to pursue the user s gaze is one area of researches that has received attention in the researchers []. In order to pursue the position of gaze, basicall the detection of the user s face and ees and the localization of pupil are important. Feng and Yuen suggested the method to detect the ee b using VPF (Variance Projection Function) []. This method has weak point that is difficult to detect the ee from the image in the case of wearing the glass or small ees. Asadifard and Shanbezadeh detected the ees and calculated the pupil center b using CDF (Cumulative Densit Function) [3]. This method ma detect pupil centers incorrectl because of the eebrows, hair, and lighting reflected in the ees. Therefore, this paper aims to suggest the method of detecting the pupil center which can be applied in the situations such as the noise and lighting in real time. In Section, the background of this stud was eplained. Section describes how to detect the pupil center using VPF. Section 3 presents the method of updating the pupil Corresponding author ISSN: ASTL Copright 04 SERSC

2 Vol.49 (ICSS 04) center information b using the edge and circle characteristic. In Section 4, the eperimental results are eplained to evaluate the performance of the proposed method, and the conclusion is described in Section 5. Pupil Center Detection The overall flow of the suggested pupil center detection algorithm is shown in Fig.. First, the user s face is detected from the input image, and the ee area is etracted from the detected face. The initial pupil center is then detected b using VPF from the etracted ee area. The edge is detected based on the detected pupil center, and the valid points are selected among the detected edges. Subsequentl, the vertical bisectors between the points are etracted and accumulated, and the center locations of the accumulated points in the top 5% are used to update the pupil center. Fig.. Overall flow of the pupil center detection In order to calculate the vertical bisector of chord, the initial pupil central point should be etracted. First, VPF is used for calculating the initial pupil center. Generall, the VPF value will become big if there is the great difference between the gra value of relevant piel and the average gra value of overall image, and the VPF value will become small if the gra value of relevant piel is similar with the average gra value. VPF is calculated through formula () and (). v = [ I(, i ) Vm ( ) ] i = () h = [ I( i, ) H m( ) ] i = () 54 Copright 04 SERSC

3 Vol.49 (ICSS 04) ( ) = m i = I(, ) V (3) m ( ) = i = i I(, ) H (4) In formula () and (), δ v and δ h mean the vertical and horizontal VPF, I means the gra scale image, and V() and H() mean the sum of the light intensit of and direction. The initial pupil center coordinates initpc and initpc can be calculated as follows b finding the smallest valle from the vertical and horizontal VPF. initpc + ( V V ) / V ( V V) / V i = (5) initpc + = (6) In formula (5) and (6), V and V mean the valle of the vertical VPF, V and V mean the valle of the horizontal VPF. 3 Pupil Center Updating The pupil center detection method using VPF is sensitive for the noise of image, and it cannot obtain the accurate information. Therefore, the pupil center should be updated b using the edge of image and the circle characteristic information. The pupil center updating process is as follows. The edge point pedge i (,) can be eplored b the straight line direction and rotating in α (0 ) based on initpc,, as in formula (7) true if ( Ll, Ll ) > th pedge (, ) = false otherwise i (7) In formula (7), L l means the coordinates of the l-th straight line which is eplored based on initpc,, and the th means the threshold value. In the proposed method, the corresponding location is set as the edge point when the gra value of corresponding location is bigger than the threshold value, and the threshold value should be set as the average gra value until L l- which is eplored from initpc,. There is the case that the edge point cannot be found from the inside pupil due to the noise caused b the light reflected in the ee. To solve this problem, beginning the searched edge point, the edge point should be re-eplored b using formula (7). Finall, initpc, as the initial pupil center can be calculated again b using the average location of the found pedge i (,). ' initpc = AVERAGE( pedge) (8) In formula (8), the AVERAGE means a function for calculating a location of average of edge points. After calculating the distance between the edge points based Copright 04 SERSC 55

4 Vol.49 (ICSS 04) on initpc, and the points satisfing the conditions below can be etracted b the effective points, pedge i. ' initpc pedgei dis (9) ' AND initpc pedgei In formula (9), dis and dis mean the threshold values, and the can be calculated b using the average distance between initpc and pedge i (,). Thus, after calculating the vertical bisector between the points based on the valid points selected, twodimensional arra should be accumulated. Finall, the pupil center can be calculated b finding the cumulative position from the cumulative arra. The method calculates the center position of the top 5% of accumulated arra as the pupil center. dis 4 Eperimental Results The tpes of PC used in the test are Intel Core i7-600 CPU, 4G RAM, and Samsung SSD 50G. As the eperimental data, the BioID Face DataBase [4] was used for evaluating the performance. Database is configured with,5 gra scales of images of size. The face and the ee area of image were detected b using MCT (Modified Census Transform) and Adaboost classifier [5] which was suggested b Froba and Ernst. In order to evaluate the performance of the proposed algorithm, the error d ee of the detected pupil center is calculated as in formula (0). d ee ma = ( C C ', C C ' ) l l l C C In formula (0), C l and C r mean the ground truth based on the left and right pupil, and C l mean C r the left and right pupil center coordinates detected b the suggested algorithm. r r r (0) Fig.. Performance comparison 56 Copright 04 SERSC

5 Vol.49 (ICSS 04) Fig. shows the performance comparison with other algorithms. The eisting algorithms used for the performance comparison in this paper were D Adaboost [6], GPF [7], and CDF. The provided the results of test ecept the images of the closedees and wearing the glass, and thus the suggested method conducted the test in the same environment. In the result of the test about the 46 images of wearing the glass, the proposed method showed the detection rate of 88%. Fig. 3 shows the result images of the detection of the pupil center. The green circles in Fig. 3 means the ground truth of the pupil center, and the red crosses mean the center position of the detected pupil. Fig. 3. Detected pupil centers 5 Conclusion In this paper, the initial pupil center coordinates were calculated through the projection using the dispersion of the gra value in order to detect the pupil center in the case of image including the noise. The edge points were then found and the circle was eplored for updating the pupil center coordinates. As a result, the suggested algorithm shows the higher detection rate than other algorithms, and the eecution speed was suitable for the real time. However, in the case of wearing the glass, the detection rate decreased. It ma be caused b the process of eploration of the initial central point or circle in regard to the reflected lighting of the glass and the glass frame. Therefore, solving this problem must be the goal of the research in the future. Acknowledgement. This work was supported b the Global Leading Technolog Program of the Office of Strategic R&D Planning (OSP) funded b the Ministr of Trade, Industr & Energ, Republic of Korea. (00448) References. Al-Rahafeh, A., Faezipour, M.: Ee Tracking and Head Movement Detection: A State-of- Art Surve. IEEE Journal of Translational Engineering in Health and Medicine, vol., (03). Feng, C., Yuen, C.: Variance Projection Function and Its Application to Ee Detection for Human Face Recognition. Pattern Recognition, vol, 9, no. 9, pp (998). Copright 04 SERSC 57

6 Vol.49 (ICSS 04) 3. Asadifard, M., Shanbezadeh, J.: Automatic Adaptive Center of Pupil Detection Using Face Detection and CDF Analsis. In: The International MultiConference of Engineers and Computer Scientists (IMECS), vol., pp (00) 4. BioID Face Database, 5. Froba, B., Ernst, A.: Face Detection with the Modified Census Transform. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp (004) 6. Niu, Z., Shan, S., Yan, S., Chen, X., Gao, W.: D Cascaded AdaBoost for Ee Localization. In: IEEE International Conference on Pattern Recognition, vol., pp (006) 7. Zhou, Z. H., Geng, X.: Projection Functions for Ee Detection. In: IEEE International Conference on Pattern Recognition, vol. 37, no 5, pp (004) 58 Copright 04 SERSC