Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Face and Iris Detection Algorithm based on SURF and circular Hough Transform

서프 및 하프변환 기반 운전자 동공 검출기법

  • Artem, Lenskiy (School of Electrical, Electronics and IT, University of Ulsan) ;
  • Lee, Jong-Soo (School of Computer Engineering and Information Technology, University of Ulsan)
  • 아텀 렌스키 (울산대학교 전기전자정보시스템공학부) ;
  • 이종수 (울산대학교 컴퓨터정보통신공학부)
  • Received : 2010.04.21
  • Published : 2010.09.25
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Abstract

The paper presents a novel algorithm for face and iris detection with the application for driver iris monitoring. The proposed algorithm consists of the following major steps: Skin-color segmentation, facial features segmentation, and iris positioning. For the skin-segmentation we applied a multi-layer perceptron to approximate the statistical probability of certain skin-colors, and filter out those with low probabilities. The next step segments the face region into the following categories: eye, mouth, eye brow, and remaining facial regions. For this purpose we propose a novel segmentation technique based on estimation of facial class probability density functions (PDF). Each facial class PDF is estimated on the basis of salient features extracted from a corresponding facial image region. Then pixels are classified according to the highest probability selected from four estimated PDFs. The final step applies the circular Hough transform to the detected eye regions to extract the position and radius of the iris. We tested our system on two data sets. The first one is obtained from the Web and contains faces under different illuminations. The second dataset was collected by us. It contains images obtained from video sequences recorded by a CCD camera while a driver was driving a car. The experimental results are presented, showing high detection rates.

본 논문에서는 얼굴과 동공을 검색하는 새로운 기법을 제시하며, 안전운행을 위한 운전자의 동공 감시에 적용한 실험결과를 포함하고 있다. 제시된 기법은 세 단계 주요 과정을 거치는데, 먼저 스킨칼라 세그먼테이션 기법으로 얼굴을 찾는 과정으로 이는 지금까지 사용된 휴리스틱모델이 아닌 학습과정 모델에 기반을 두고 있다. 다음에 얼굴 특징 세그먼테이션으로 눈, 입, 눈썹 등의 부분을 검출 하는데, 이를 위해 얼굴 각 부분에서 추출한 고유 특징들에 대한 PDF 추정을 사용하고 있다. 마지막으로 서큘러 하프 변환기법으로 눈 안의 동공을 찾아낸다. 제시된 기법을 조명이 다른 웹 얼굴 영상과 운전자의 CCD 얼굴 영상에 적용하여 동공을 찾아내는 실험을 하여, 높은 동공 검출율을 확인하였다.

Keywords

References

  1. Nakayama, M., Katahara, S., Aoki, M., Detection of driver's hazardous situation using coarse far-infrared image, Intelligent Vehicles Symposium, 2008 IEEE , vol., no., pp.325-328, 4-6 June 2008.
  2. Riad I. Hammoud, G. Witt, R. Dufour, A. Wilhelm, T. Newman, On Driver Eye Closure Recognition for Commercial Vehicles, Proceedings of SAE Commercial Vehicles Engineering Congress and Exhibition, Chicago, IL, USA, Oct 8, 2008.
  3. Batista Jorge P., A Real-Time Driver Visual Attention Monitoring System IbPRIA 2005, LNCS 3522, pp. 200-208, 2005.
  4. Zhao Shuyan and Grigat Rolf-Rainer. 2006. Robust Eye Detection under Active Infrared Illumination, Pattern Recognition. ICPR 2006. Vol. 4 pp. 481-484.
  5. Wang Rong-ben, Guo Ke-you, Shi Shu-ming, Chu Jiang-wei, A monitoring method of driver fatigue behavior based on machine vision, Intelligent Vehicles Symposium, 2003. Proceedings. IEEE , vol., no., pp. 110-113.
  6. Jee Hyungkeun, Lee Kyunghee, Pan Sungbum, Eye and face detection using SVM, Conference on Intelligent Sensors, Sensor Networks and Information, 2004, pp. 577-580.
  7. Rowley H.A., Baluja S., Kanade T., Neural Network- Based Face Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, Vol. 20 , Issue 1, pp. 23-38. https://doi.org/10.1109/34.655647
  8. R. Motwani, M. Motwani, F. Harris, Eye Detection using Wavelets and ANN, in Proceedings of GSPx, Santa Clara, September 2004.
  9. Kun He, Jiliu Zhou, Yu Song, Qiang Qiao, Multiresolution eye location from image, Proceedings of Signal Processing, ICSP '04. 2004, vol. 2, pp. 901-905.
  10. King-Hong Cheung, Jane You , Wai-Kin Kong, Zhang, D., A study of aggregated 2D Gabor features on appearance-based face recognition, Proceedings of Image and Graphics, 2004. pp. 310-313.
  11. Zhi-Hua Zhou, Xin Geng, Projection Functions for Eye Detection, Pattern recognition, 2004, vol. 37, n5, pp. 1049-1056 https://doi.org/10.1016/j.patcog.2003.09.006
  12. Lin Daw-Tung, Yang Chen-Ming, Real-time eye detection using face circle fitting and dark-pixel filtering, Multimedia and Expo, 2004. ICME '04. 2004 IEEE International Conference on, Vol. 2 (2004), pp. 1167-1170 Vol.2.
  13. Kim Hyoung-Joon, and Kim Whoi-Yul, Eye Detection in Facial Images Using Zernike Moments with SVM, ETRI Journal, vol.30, no.2, Apr. 2008, pp.335-337 25 https://doi.org/10.4218/etrij.08.0207.0150
  14. D'Orazio T., Leo, M., Cicirelli, G., Distante, A. 2004. An algorithm for real time eye detection in face images. Inter. Conf. on Pattern Recognition. Vol 3 pp.278-281.
  15. Perez C.A., Lazcano V.A., Estvez P.A., Real-Time Iris Detection on Coronal-Axis- Rotated Faces, IEEE Transactions on Systems, Man and Cybernetics - Part C-Applications and Reviews, Vol 37,No.5, pp.971-978. Sept. 2007, https://doi.org/10.1109/TSMCC.2007.900647
  16. Duda R. O., and Hart P. E. 1972. Use of the Hough Transformation to Detect Lines and Curves in Pictures. Communications of Association for Computing Machinery, 15(1), pp.11-15. https://doi.org/10.1145/361237.361242
  17. Markus Weber, "Frontal face dataset", California Institute of Technology, 2003, http://www.vision.caltech.edu/htmlfiles/archive.html.
  18. Tran Le Hong Du, Duong Anh Duc, Duong Nguyen Vu, Ridge and Valley based Face Detection, International Conference on Research, Innovation and Vision for the Future, 2006.
  19. Dazhi Zhang, Boying Wu, Jiebao Sun, Qinglei Liao A Face Detection Method Based on Skin Color Model, Proceedings of the 11th Joint Conference on Information Sciences (2008).