KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

A New Confidence Measure for Eye Detection Using Pixel Selection

눈 검출에서의 픽셀 선택을 이용한 신뢰 척도

  • Received : 2015.04.01
  • Accepted : 2015.06.17
  • Published : 2015.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a new confidence measure using pixel selection for eye detection and design a hybrid eye detector. For this, we produce sub-images by applying a pixel selection method to the eye patches and construct the BDA(Biased Discriminant Analysis) feature space for measuring the confidence of the eye detection results. For a hybrid eye detector, we select HFED(Haar-like Feature based Eye Detector) and MFED(MCT Feature based Eye Detector), which are complementary to each other, as basic detectors. For a given image, each basic detector conducts eye detection and the confidence of each result is estimated in the BDA feature space by calculating the distances between the produced eye patches and the mean of positive samples in the training set. Then, the result with higher confidence is adopted as the final eye detection result and is used to the face alignment process for face recognition. The experimental results for various face databases show that the proposed method performs more accurate eye detection and consequently results in better face recognition performance compared with other methods.

본 논문에서는, 눈 검출에서의 픽셀 선택 방법을 이용한 편향 판별 분석(BDA) 기반의 신뢰 척도를 제안하고 이를 이용하여 hybrid 눈 검출기를 설계한다. 이를 위해 눈 조각 영상에서 먼저 판별 분석에 유용한 픽셀들을 선택하여 부분 영상을 만들고, 부분 영상에 BDA를 적용하여 신뢰 척도를 위한 특징 공간을 구성한다. Hybrid 눈 검출기를 구성하는 기본 검출기로는 상호 보완적인 특성을 가진 HFED와 MFED를 사용하였다. 주어진 영상에 대해, 기본 검출기들에 의해 생성된 눈 좌표를 가지고 생성한 눈 조각 영상의 부분 영상들을 BDA 특징공간에 투영하여 positive 샘플의 평균과의 거리를 측정함으로써 그 정확성을 측정하고, 기본 검출기의 결과들 중에서 신뢰도가 높은 결과를 최종 눈 검출 결과로 사용한다. 다양한 얼굴 데이터베이스들에 대한 실험 결과에서, 제안한 방법은 검출된 눈 좌표의 정확도 측면에서 뿐만 아니라 검출된 눈 좌표를 이용한 얼굴 인식 성능에서도 다른 방법들보다 우수한 결과를 나타내었다.

Keywords

References

  1. M. A. Turk and A. P. Pentland, "Face recognition using eigenfaces," in Computer Vision and Pattern Recognition, pp.586-591, 1991.
  2. P. N. Belhumeur, J. P. Hespanha, and D Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on : Pattern Analysis and Machine Intelligence, Vol.19, No.7, pp.711-720, 1997. https://doi.org/10.1109/34.598228
  3. H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, "Discriminative common vectors for face recognition," IEEE Transactions on : Pattern Analysis and Machine Intelligence, Vol.27, No.1, pp.4-13, 2005. https://doi.org/10.1109/TPAMI.2005.9
  4. H. Yu and J. Yang, "A direct LDA algorithm for high-dimensional data with application to face recognition," Pattern recognition, Vol.34, No.10, pp.2067-2070, 2001. https://doi.org/10.1016/S0031-3203(00)00162-X
  5. C. H. Kim and C. H. Choi, "Image covariance-based subspace method for face recognition," Pattern Recognition, Vol.40, No.5, pp.1592-1604, 2007. https://doi.org/10.1016/j.patcog.2006.09.010
  6. S. Shan, Y. Chang, W. Gao, B. Cao, and P. Yang. "Curse of misalignment in face recognition: problem and a novel mis-alignment learning solution," in Proceeding of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp.314-320, 2004.
  7. P. Viola and M. J. Jones, "Robust real-time face detection," International Journal of Computer Vision, Vol.57, No.2, pp.137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  8. B. Froba and A. Ernst, "Face detection with the modied census transform," in Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp.91-96, 2004.
  9. S. I. Choi, Y. G. Lee, and C. H. Kim, "Confidence Measure Using Composite Features for Eye Detection in a Face Recognition System," IEEE Signal Processing Letters, Vol.22, No.2, pp.225-228, 2015. https://doi.org/10.1109/LSP.2014.2335198
  10. C. H. Kim, S. I. Choi, M. Turk, and C. H. Choi, "A new biased discriminant analysis using composite vectors for eye detection," IEEE Transactions: Systems, Man, and Cybernetics, Part B: Cybernetics, Vol.42, No.4, pp.1095-1106, 2012. https://doi.org/10.1109/TSMCB.2012.2186798
  11. J. N. Jung and P. K. Rhee, "An Effective Eye Location for Face Recognition," The KIPS transactions : Part B, Vol.12, No.2, pp.109-114, 2005.
  12. S. H. Kim and G. T. Han, "An Enhanced Method for Detecting Iris from Smartphone Images in Real-Time," KIPS transactions on software and data engineering, Vol.2, No.9, pp.643-650, 2013. https://doi.org/10.3745/KTSDE.2013.2.9.643
  13. X. S. Zhou and T. S. Huang, "Small sample learning during multimedia retrieval using biasmap," in Computer Vision and Pattern Recognition, pp.1-11, 2001.
  14. P. Phillips, H. Moon, S. Rizvi, and P. Rauss, "The FERET Evaluation Methodology for Face Recognition Algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.10, pp.1090-1104, 2000. https://doi.org/10.1109/34.879790
  15. A. M. Martnez and R. Benevente, The AR face database, CVC Technical Report #24, Computer Vision Center (CVC), Barcelona, Spain.
  16. T. Sim, S. Baker, and M. Bsat, "The CMU pose, illumination, and expression (PIE) database," in Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp.46-51, 2002.
  17. D. J. Kim, S. H. Lee, and M. K. Sohn, "Face Recognition using Modified Local Directional Pattern Image," KIPS transactions on software and data engineering, Vol.2, No.3, pp.205-208, 2013. https://doi.org/10.3745/KTSDE.2013.2.3.205
  18. J. H. Lee, "Video-Based Face Recognition by Discriminant Analysis using Distance From Feature Space of each pose," Master. dissertation, National Seoul University, KOREA, 2011.
  19. S. I. Choi, C. H. Choi, G. M. Jeong, and N. J. Kwak, "Pixel selection based on discriminant features with application to face recognition," Pattern Recognition Letters, Vol.33, No.9, pp.1083-1092, 2012. https://doi.org/10.1016/j.patrec.2012.01.005
  20. R. Duda, P. Hart, and D. Stork, "Pattern Classification," 2nd ed., New York: John Wiley&Sons, 2001.
  21. J. Song, Z. Chi, and J. Liu, "A robust eye detection method using combined binary edge and intensity information," Pattern Recognition, Vol.39, No.6, pp.1110-1125, 2006. https://doi.org/10.1016/j.patcog.2005.11.015