臺灣博碩士論文加值系統

在許多交通事故中，疲勞駕駛導致意外發生的事件層出不窮。於駕駛環境下，使用不妨礙駕駛人的視訊設備，觀測駕駛者眼睛的狀況，以達成瞌睡偵測是最直接有效的。亦即，經由偵測人眼開闔狀態來判斷是否出現瞌睡情形是相當準確可靠之方法。在此篇論文中，我們以彩色CCD 攝影機做為影像輸入來源，經過膚色判定來區分出人臉區域，再以主成份分析法擷取出眼部區域，再利用圓形可變樣版搜尋此區域，找出瞳孔的位置並加以分析之，以此判定眼睛此時的張開狀態。經實驗證明，我們提出的方法於判定眼睛狀態的準確度相當高，將有助於提高昏睡偵測系統的成功率。

Sleepiness and driving is a dangerous combination, drowsy driving can be just as fatal. Accordingly, it is necessary to develop a drowsy driver awareness system. To avoid interrupting the driver, it is necessary to build a system under non-invasive and non-contact condition. Image process system suits to achieve such a request. Hence, it is
recommended to judge the drowsiness state by observing eye status of operators via eye video. In this thesis, we use a CCD camera as the image sources and use skin color map to segment skin region. Then we use PCA algorithm to find the eye region for circular template searching. The circular template will locate the iris region and finally we can analyze this region to classify the eye openness state. By numerical simulation, we have obtained a high accuracy on eye openness detection and it would be helpful for the drowsy detection system.

Contents

摘要 ……………………………………………………………………i
ABSTRACT ……………………………………………………………ii
ACKNOWLEDGEMENT ……………………………………………………iv
CONTENTS ………………………………………………………………v
LIST of FIGURES……………………………………………………vii
LIST of TABLES………………………………………………………vii

CHAPTER 1 INTRODUCTION ……………………………………………1

1.1 Motivation…………………………………………………………2
1.2 Face Segmentation…………………………………………………2
1.3 Eye Detection………………………………………………………4
1.4 Iris Extraction……………………………………………………4
1.5 Eye State Determination…………………………………………5
1.6 Thesis Outline……………………………………………………5

CHAPTER 2 FACE SEGMENTATION…………………………………………6

2.1 YCbCr Color Space…………………………………………………6
2.2 Face Segmentation Algorithm……………………………………7

CHAPTER 3 EYE DETECTION AND IRIS EXCTRATION…………………14

3.1 PCA Review…………………………………………………………14
3.2 Computation of Eigeneyes ……………………………………15
3.3 Representing Eyes onto This Basis…………………………17
3.4 Eye Region Recognition Using Eigeneyes……………………17
3.5 Using Deformable Template for Iris Extraction…………18
3.5.1 Intensity Field and Edge Field……………………………19
3.5.2 Anisotropic Diffusion………………………………………20
3.5.3 Circular Deformable Template………………………………24

CHAPTER 4 EYE STATE DETERMINATION………………………………28

4.1 Analyzing the Iris Region……………………………………28
4.2 Determining Eye State…………………………………………30
4.3 Drowsy State Detection………………………………………32

CHAPTER 5 EXPERIMENT RESULT………………………………………34

5.1 Exper iment Setting..…………………………………………34
5.2 Exper iment Result..…………………………………………35

CHAPTER 6 CONCLUSIONS AND FUTURE WORK……………………… 42

REFERENCES ……………………………………………………………43

References
[1] A. Yuille, P. Hallinan, and D. Cohen, “Feature Extraction from Faces Using Deformable Templates,” International Journal of Computer Vision, vol. 8, no. 2, pp. 399–111, 1992.

[2] J. Deng and F. Lai, “Region-Based Template Deformation and Masking for Eye-feature Extraction and Description,” Pattern Recognition, vol. 30, no. 3, pp. 403–419, March 1997.

[3] W.W Weirwille., “Overview of Research on Driver Drowsiness Definition and Driver Drowsiness Detection,” in Proc. 14th International Technical Conference on Enhanced Safety of Vehicles, pp. 23-26, Munich, Germany, May, 1994.

[4] E. Hjelmas and B. K. Low, “Face detection: A survey,” Computer Vision and Image Understanding, vol. 83, pp. 236–274, 2001.

[5] R. Feraud, O. J. Bernier, J. Viallet, and M. Collobert, “A Fast and Accurate Face Detector. Based on Neural Networks,” IEEE Trans. Patt. Anal. Machine Intell., vol.
23, no. 1, January 2001.

[6] D. Maio and D. Maltoni, “Real-time face location on gray-scale static images,”Pattern Recognition, vol. 33, pp. 1525–1539, 2000.

[7] C. Garcia and G. Tziritas, “Face detection using quantized skin color regions merging and wavelet packet analysis.” IEEE Trans. Multimedia, vol. 1, no. 3, pp.
264-277, 1999.

[8] H. Wu, Q. Chen, and M. Yachida, “Face detection from color images using a fuzzy pattern matching method,” IEEE Trans. Patt. Anal. Machine Intell., vol. 21, pp. 557–563, 1999.

[9] H. Rowley, S. Baluja, T. Kanade, “Neural network-based face detection.” IEEE Trans. Patt. Anal. Machine Intell., vol. 20, no. 1, pp. 23－38, 1998.

[10] K. Sung and T. Poggio, “Example-based learning for view based human face detection. ” IEEE Trans. Patt. Anal. Machine Intell., vol. 20, no. 1, 39－51, 1998.

[11] M. Yang and N. Ahuja, “Detecting human faces in color images.” in Proc. IEEE Conference on Image Processing, Chicago, pp. 127－139, 1998.

[12] A. Colmenarez and T. Huang, “Face Detection with Information-Based Maximum Discrimination.” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PuertoRico, 782–787, 1997.

[13] K.C. Yow and Cipolla, “Feature-Based Human Face Detection,” Technical Report CUED/F-INFENG, TR 249, Dept. of Eng., Univ. of Cambridge, England, 1996.

[14] M. S. Lew and N. Huijsmans, “Information Theory and Face Detection,” in Proc. 13th International Conference on Pattern Recognition (ICPR'96), Vol. 3, pp.601, 1996.

[15] G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman, and T. J. Sejnowski, “Classifying facial actions,” IEEE Trans. Patt. Anal. Machine Intell., vol. 21, no. 10, pp.
974–989, October 1999.

[16] J. Huang and Y. Wang, “Compression of color facial images using feature correction two-stage vector qantization Image Processing,” IEEE Trans. Image Processing,Vol 8, no. 1, pp. 102–109, Jan. 1999.

[17] D. Chai and K. N. Ngan, “Face segmentation using skin-color map in videophone applications,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, pp. 551–564, 1999.

[18] T. Kurozumi, Y. Shinza, Y. Kenmochi, and K. Kotani, “Facial individuality and expression analysis by eigenspace method based on class features or multiple
discriminant analysis,” in Proc. International Conference on Image Processing, vol. 1, pp. 648–652, 1999.

[19] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 19, pp. 743–756, 1997.

[20] L. H. Staib and J. S. Duncan, “Boundary finding with parametrically deformable models, ” IEEE Trans. Patt. Anal. Machine Intell., vol. 14, no. 11, pp. 1061–1075, 1992.

[21] M. P. D. Jolly, S. Lakshmanan, and A. K. Jain, “Vehicle segmentation and classification using de-formable templates,” IEEE Trans. Patt. Anal. Machine Intell.,
vol. 18, no. 3, pp. 293–308, 1996.

[22] Harris and M. Stephens, “A combined corner and edge detector,” in Proc. 4th Alvey Vision Conference, pp. 147–151, 1988.

[23] Perona and J. Malik, “Scale space and edge detection using anisotropic diffusion,”IEEE Trans. Pattern Anal. Machine Intell., vol. 12, no. 7, pp. 629–639, July 1990.

[24] Canny, “A computational approach to edge detection,” IEEE Trans. Patt. Anal. Machine Intell., vol. 8, no. 6, pp. 679–698, Nov. 1986.

[25] T. Moriyama, T. Kanade, J. Xiao, and J. Cohn, “Meticulously Detailed Eye Region Model and Its Application to Analysis of Facial Images,” IEEE Trans. Patt. Anal. Machine Intell., vol. 28, no. 5, pp.738–752, 2006.