Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

  • Received : 2015.05.15
  • Accepted : 2015.10.15
  • Published : 2016.09.30
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Abstract

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

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References

  1. A. Lanitis, "Facial biometric templates and aging: problems and challenges for artificial intelligence," in Proceedings of 5th IFIP Conference on Artifical Intelligence Applications & Innovaions (AIAI), Thessaloniki, Greece, 2009, pp. 142-149.
  2. K. Ricanek and T. Tesafaye, "Morph: a longitudinal image database of normal adult age-progression," in Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK, 2006, pp. 341-345.
  3. FG-NET aging database [Online]. Available: http://www.fgnet.rsunit.com.
  4. N. Ramanathan, R. Chellappa, and S. Biswas, "Age progression in human faces: a survey," Journal of Visual Languages and Computing, vol. 15, pp. 3349-3361, 2009.
  5. T. F. Cootes, G. J. Edwards, and C. J. Taylor, "Active appearance models," IEEE Transactions on pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681-685, 2001. https://doi.org/10.1109/34.927467
  6. M. H. Yang, "Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods," in Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition, Washington, DC, 2002, pp. 215-220.
  7. N. Ramanathan and R. Chellappa, "Modeling shape and textural variations in aging faces," in Proceedings of the 8th International Conference on Automatic Face and Gesture Recognition, Amsterdam, Netherlands, 2008, pp. 1-8.
  8. U. Park, Y. Tong, and A. K. Jain, "Age-invariant face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 947-954, 2010. https://doi.org/10.1109/TPAMI.2010.14
  9. H. Ling, S. Soatto, N. Ramanathan, and D. W. Jacobs, "A study of face recognition as people age," in Proceedings of the 11th International IEEE Conference on Computer Vision, Rio de Janeiro, 2008, pp. 1-8.
  10. H. Ling, S. Soatto, N. Ramanathan, and D. W. Jacobs, "Face verification across age progression using discriminative methods," IEEE Transactions on Information Forensics and Security, vol. 5, no. 1, pp. 82-91, 2010. https://doi.org/10.1109/TIFS.2009.2038751
  11. G. Mahalingam and C. Kambhamettu, "Face verification with aging using AdaBoost and local binary patterns," in Proceedings of the 7th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), Chennai, India, 2010, pp. 101-108.
  12. R. Singh, M. Vatsa, A. Noore, and S. K. Singh, "Age transformation for improving face recognition performance," in Proceedings of 2nd International Conference on Pattern Recognition and Machine Intelligence (PReMI), Kolkata, India, 2007, pp. 576-583.
  13. S. Biswas, G. Aggarwal, N. Ramanathan, and R. Chellappa, "A non-generative approach for face recognition across aging," in Proceedings of the 2nd IEEE International Conference on Biometrics: Theory, Application and Systems (BTAS), Arlington, VA, 2008, pp. 1-6.
  14. G. Mahalingam and C. Kambhamettu, "Age invariant face recognition using graph matching," in Proceedings of the 4th IEEE International Conference on Biometrics: Theory, Application and Systems (BTAS), Washington, DC, 2010, pp. 1-7.
  15. M. Bereta, P. Karczmarek, W. Pedrycz, and M. Reformat, "Local descriptors in application to the aging problem in face recognition," Pattern Recognition, vol. 46, no. 10, pp. 2634-2646, 2013. https://doi.org/10.1016/j.patcog.2013.03.010
  16. D. Sungatullina, J. Lu, G. Wang, and P. Moulin, "Multiview discriminative learning for age-invariant face recognition," in Proceedings of the 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, Shanghai, China, 2013, pp. 1-6.
  17. L. Wiskott, J. M. Fellous, N. Kuiger, and C. Von Der Malsburg, "Face recognition by elastic bunch graph matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, 1997. https://doi.org/10.1109/34.598235
  18. H. Yang, D. Huang, and Y. Wang, "Age invariant face recognition based on texture embedded discriminative graph model," in Proceedings of 2014 IEEE International Joint Conference on Biometrics (IJCB), Clearwater, FL, 2014, pp. 1-8.
  19. F. Juefei-Xu, K. Luu, M. Savvides, T. D. Bui, and C. Y. Suen, "Investigating age invariant face recognition based on periocular biometrics," in Proceedings of 2011 IEEE International Joint Conference on Biometrics (IJCB), Washington, DC, 2011, pp. 1-7.
  20. T. F. Cootes and C. J. Taylor, "Statistical models of appearance for computer vision," Department of Imaging Science and Biomedical Engineering, University of Manchester, UK, 2004.
  21. Z. M. Hafed and M. D. Levine, "Face recognition using the discrete cosine transform," International Journal of Computer Vision, vol. 43, no. 3, pp. 167-188, 2001. https://doi.org/10.1023/A:1011183429707
  22. J. Jiang and G. Feng, "Robustness analysis on facial image description in DCT domain," Electronics Letters, vol. 43, no. 24, pp. 1354-1355, 2007. https://doi.org/10.1049/el:20072735
  23. K. R. Rao and P. Yip, Discrete Cosine Transform: Algorithms, Advantages, Applications. Boston, MA: Academic Press, 1990.
  24. W. B. Pennebaker and J. L. Mitchell, JPEG: Still Image Data Compression Standard. New York: Springer, 1993.
  25. W. Chen, M. J. Er, and S. Wu, "Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 36, no. 2, pp. 458-466, 2006. https://doi.org/10.1109/TSMCB.2005.857353
  26. Y. Wang and Q. Ruan, "Kernel fisher discriminant analysis for palmprint recognition," in Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, 2006, pp. 457-460.
  27. C. Liu, "Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp. 725-737, 2006. https://doi.org/10.1109/TPAMI.2006.90
  28. Q. Liu, H. Lu, and S. Ma, "Improving kernel Fisher discriminant analysis for face recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 42-49, 2004. https://doi.org/10.1109/TCSVT.2003.818352
  29. P. N. Belhumeur, J. P. Hespanha, and D. J. 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
  30. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, "The FERET evaluation methodology for face-recognition algorithms," IEEE Transactions on Pattern Recognition and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, 2000. https://doi.org/10.1109/34.879790
  31. V. Struc and N. Pavesic, "The complete gabor-fisher classifier for robust face recognition," EURASIP Journal on Advances in Signal Processing, vol. 2010, article ID. 847680, 2010.
  32. V. Struc, and N. Pavesic, "Gabor-based kernel partial-least-squares discrimination features for face recognition," Informatica, vol. 20, no. 1, pp. 115-138, 2009.
  33. A. Lanitis, C. J. Taylor, and T. F. Cootes, "Toward automatic simulation of aging effects on face images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 442-455, 2002. https://doi.org/10.1109/34.993553
  34. N. Ramanathan and R. Chellappa, "Modeling age progression in young faces," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, 2006, pp. 387-394, 2006.
  35. J. Wang, Y. Shang, G. Su, and X. Lin, "Age simulation for face recognition," in Proceedings of the 18th IEEE International Conference on Pattern Recognition, Hong Kong, 2006, pp. 913-916.
  36. X. Geng, Z. H. Zhou, and K. Smith-Miles, "Automatic age estimation based on facial aging patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2234-2240, 2007. https://doi.org/10.1109/TPAMI.2007.70733
  37. U. Park, Y. Tong, and A. K. Jain, "Face recognition with temporal invariance: a 3D aging model," in Proceedings of the 8th IEEE International Conference on Automatic Face & Gesture Recognition, Amsterdam, 2008, pp. 1-7.
  38. Z. Li, U. Park, and A. K. Jain, "A discriminative model for age invariant face recognition," IEEE Transactions on Information Forensics and Security, vol. 6, no. 3, pp. 1028-1037, 2011. https://doi.org/10.1109/TIFS.2011.2156787