Gray Hausdorff distance measure for comparing face images

IEEE Transactions on Information Forensics and Security

Volumn 1, Issue 3, 2006, Pages 342-349

  Vivek, E P ^a   Sudha, N ^b  

^a Synopsys India Pvt Ltd   (India)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Euclidean distance transform; Face image; Face recognition; Gray Hausdorff distance

Indexed keywords

ALGORITHMS; BENCHMARKING; DATABASE SYSTEMS; IMAGE ANALYSIS; PATTERN RECOGNITION SYSTEMS;

EUCLIDEAN DISTANCE TRANSFORM; EXPRESSION VARIATIONS; FACE IMAGE; GRAY HAUSDORFF DISTANCE;

FACE RECOGNITION;

EID: 33748120010     PISSN: 15566013     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIFS.2006.879294     Document Type: Article

References (19)

1. R. Shonkwiler, "An image algorithm for computing the Hausdorff distance efficiently in linear time," Inform. Process. Lett., vol. 30, pp. 87-89, Jan. 1989.
2. _, "Computing the Hausdorff set distance in linear time for any 1p point distance," Inform. Process. Lett., vol. 38, pp. 201-207, May 1991.
3. W. J. Rucklidge, "Locating objects using the Hausdorff distance," in ICCV 95: Proc. 5th Int. Conf. Computer Vision, Washington, D.C., Jun. 1995, pp. 457-464.
4. D. P. Huttenlocher, G. A. Klanderman, and W. A. Rucklidge, "Comparing images using the Hausdorff distance," IEEE Trans. Pattern Anal. Mach. Intell., vol. 15, no. 9, pp. 850-863, Sep. 1993.
5. B. Takacs and H. Wechsler, "Fast searching of digital face libraries using binary image metrics," in Proc. ICPR, 1998, pp. 1235-1237.
6. E. Sánchez-Nielsen, J. Lorenzo-Navarro, and M. Hernández-Tejera, "Increasing efficiency of Hausdorff approach for tracking real scenes with complex environments," in Proc. ICIAP, 2001, pp. 131-136.
7. S. H. Kim and R.-H. Park, "An efficient algorithm for video sequence matching using the modified Hausdorff distance and the directed divergence," IEEE Trans. Circuits Syst. Video Technol., vol. 12, no. 7, pp. 592-596, Jul. 2002.
8. B. Takacs, "Comparing face images using the modified Hausdorff distance," Pattern Recognit., vol. 31, no. 12, pp. 1873-1881, 1998.
9. B. Guo, K.-M. Lam, K.-H. Lin, and W.-C. Siu, "Human face recognition based on spatially weighted Hausdorff distance," Pattern Recognit. Lett., vol. 24, pp. 499-507, Jan. 2003.
10. K.-H. Lin, K.-M. Lam, and W.-C. Siu, "Spatially eigen-weighted Hausdorff distances for human face recognition," Pattern Recognit., vol. 36, pp. 1827-1834, Aug. 2003.
11. Y. Gao and M. K. Leung, "Face recognition using line edge map," IEEE Trans. Pattern Anal. Machine Intell., vol. 24, pp. 764-779, Jun. 2002.
12. Y. Gao, "Efficiently comparing face images using a modified Hausdorff distance," Proc. Inst. Elect, Eng. Vision, Image and Signal Processing, vol. 150, pp. 346-350, Dec. 2003.
13. P. Sinha, "Recognizing complex patterns," Nature Neurosci., vol. 5, pp. 1093-1097, Nov. 2002.
14. F. Y. Shih and Y.-T. Wu, "The efficient algorithms for achieving Euclidean distance transformation," IEEE Trans. Image Process., vol. 13, no. 8, pp. 1078-1091, Aug. 2004.
15. T. H. Cormen, C. Stein, R. L. Rivest, and C. E. Leiserson, Introduction to Algorithms, 2nd ed. New York: McGraw-Hill, 2001.
16. Yale University Face Database [Online]. Available: http://cvc.yale.edu/ projects/yalefaces/yalefaces.html.
17. Bern University Face Database [Online]. Available: ftp://ftp.iam.unibe. ch/pub/images/faceimages/.
18. A. Martinez and R. Benavente, The AR Face Database 1998, CVC Tech. Rep. 24.
19. The ORL Database of Faces [Online]. Available: http://www.uk.re-search. att.com/facedatabase.html.