Performance Improvement for Robust Eye Detection Algorithm under Environmental Changes

환경변화에 강인한 눈 검출 알고리즘 성능향상 연구

  • Ha, Jin-gwan (Department of Computer Science and Engineering, Sejong University) ;
  • Moon, Hyeon-joon (Department of Computer Science and Engineering, Sejong University)
  • 하진관 (세종대학교 컴퓨터공학과) ;
  • 문현준 (세종대학교 컴퓨터공학과)
  • Received : 2016.08.26
  • Accepted : 2016.10.20
  • Published : 2016.10.28


In this paper, we propose robust face and eye detection algorithm under changing environmental condition such as lighting and pose variations. Generally, the eye detection process is performed followed by face detection and variations in pose and lighting affects the detection performance. Therefore, we have explored face detection based on Modified Census Transform algorithm. The eye has dominant features in face area and is sensitive to lighting condition and eye glasses, etc. To address these issues, we propose a robust eye detection method based on Gabor transformation and Features from Accelerated Segment Test algorithms. Proposed algorithm presents 27.4ms in detection speed with 98.4% correct detection rate, and 36.3ms face detection speed with 96.4% correct detection rate for eye detection performance.


Modified Census Transform;Gabor Transform;Features from Accelerated Segment Test;Face Detection;Eye Detection;Pupil Detection


Supported by : 농림수산식품기술기획평가원


  1. P. Jonathon, H. Moon, S. Rizvi, and P. J. Rauss. "The FERET evaluation methodology for face-recognition algorithms." Pattern Analysis and Machine Intelligence, IEEE Transactions Vol 22, No. 10 (2000): 1090-1104.
  2. FERET database
  3. XM2VTS face database.
  4. BioID face database.
  5. P. Viola, and M. Jones. "Rapid object detection using a boosted cascade of simple features." In Computer Vision and Pattern Recognition, 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, pp. I-511. IEEE, 2001.
  6. Froba, Bernhard, and Andreas Ernst. "Face detection with the modified census transform." In Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on, pp. 91-96. IEEE, 2004.
  7. R. Valenti, N. Sebe, and T. Gevers. "Combining head pose and eye location information for gaze estimation." Image Processing, IEEE Transactions Vol 21, No. 2 (2012): 802-815.
  8. Z. Qian and D. Xu. "Automatic eye detection using intensity filtering and K-means clustering." Pattern Recognition Letters 31, No. 12 (2010): 1633-1640.
  9. K. Jeong and H. Moon. "Object detection using FAST corner detector based on smartphone platforms." In Computers, Networks, Systems and Industrial Engineering(CNSI), 2011 First ACIS/JNU International Conference on, pp. 111-115. IEEE, 2011.
  10. E. Rosten and T. Drummond. "Machine learning for high-speed corner detection." In Computer Vision-ECCV 2006, pp. 430-443. Springer Berlin Heidelberg, 2006.
  11. E. Mair, G.D. Hager, D. Burschka, M. Suppa, and G. Hirzinger. "Adaptive and generic corner detection based on the accelerated segment test." In Computer Vision-ECCV 2010, pp. 183-196. Springer Berlin Heidelberg, 2010.
  12. D. G. Lowe, "Object recognition from local scale-invariant features." In Computer vision, 1999. The proceedings of the seventh IEEE international conference on, Vol. 2, pp. 1150-1157. Ieee, 1999.
  13. C. Liu, J. Yuen, A. Torralba, J. Sivic, and W. T. Freeman. "Sift flow: Dense correspondence across different scenes." In Computer Vision-ECCV 2008, pp. 28-42. Springer Berlin Heidelberg, 2008.
  14. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool. "Speeded-up robust features (SURF)." Computer vision and image understanding 110, No. 3 (2008): 346-359.
  15. E. Murphy-Chutorian, and M. M. Trivedi. "Head pose estimation in computer vision: A survey." Pattern Analysis and Machine Intelligence, IEEE Transactions Vol 31, No. 4 (2009): 607-626.