http://iet.metastore.ingenta.com
1887

Overview of research on facial ageing using the FG-NET ageing database

Overview of research on facial ageing using the FG-NET ageing database

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The face and gesture recognition network (FG-NET) ageing database was released in 2004 in an attempt to support research activities aimed at understanding the changes in facial appearance caused by ageing. Since then the database was used for carrying out research in various disciplines including age estimation, age-invariant face recognition and age progression. On the basis of the analysis of published work where the FG-NET ageing database was used, conclusions related to the type of research carried out in relation to the impact of the dataset in shaping up the research topic of facial ageing are presented. This study also includes a review of key articles from different thematic areas, where the FG-NET ageing database was used and the presentation of benchmark results. The ultimate aims of this study are to present concrete facts related to research activities in facial ageing during the past decade, provide an indication of the main methodologies adopted, present a comprehensive list of benchmark results and most importantly provide roadmaps for future trends, requirements and research directions in facial ageing.

References

    1. 1)
      • 1. Kwon, Y.H., da Vitoria Lobo, N.: ‘Age classification from facial images’. Proc. IEEE Computer Vision and Pattern Recognition, 1994, pp. 762767.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • 6. Ricanek, K.Jr., Tesafaye, T.: ‘MORPH: a longitudinal image database of normal adult age-progression’. Proc. Seventh Int. Conf. on Automatic Face and Gesture Recognition, 2006, pp. 341345.
    7. 7)
    8. 8)
    9. 9)
      • 9. Fairhurst, M.: ‘Age factors in biometric processing’ (The Institution of Engineering and Technology, Stevenage, 2013).
    10. 10)
      • 10. Ngan, M., Grotherh, P.: ‘Face recognition vendor test – performance of automated age estimation algorithms’. NIST Interagency Report, 7995, 2014.
    11. 11)
      • 11. Guo, G.: ‘Age estimation and sex classification’, in Shan, C., Porikli, F., Xiang, T., Gong, S. (Eds.): ‘Video analytics for business intelligence’ (Springer-Verlag, Berlin, 2012), pp. 101131.
    12. 12)
      • 12. Guo, G.: ‘Age prediction in face images’, in Fairhurst, M. (Ed.): ‘Age factors in biometric processing’ (IET, UK, 2013), pp. 231251.
    13. 13)
      • 13. ‘FG-NET (Face and Gesture Recognition Network)’. Available at http://www.prima.inrialpes.fr/FGnet/, accessed November 2014.
    14. 14)
    15. 15)
    16. 16)
      • 16. Bhatt, H.S., Bharadwaj, S., Singh, R., Vatsa, M.: ‘On matching sketches with digital face images’. Proc. Fourth IEEE Int. Conf. on Biometrics: Theory Applications and Systems, 2013, pp. 17.
    17. 17)
    18. 18)
      • 18. Batool, N., Chellappa, R.: ‘Modeling and detection of wrinkles in aging human faces using marked point processes’. Proc. Computer Vision–ECCV, 2012. Workshops and Demonstrations, 2012, pp. 178188.
    19. 19)
    20. 20)
      • 20. Robins, J.: ‘Children's memory for and judgment of stereotypical and counter-stereotypical favorite color information’. BS thesis, Carnegie Mellon University, 2009.
    21. 21)
      • 21. Geng, X.: ‘Facial age estimation: a data representation perspective’, in Fu, Y. (Ed): ‘Human-centered social media analytics’ (Springer International Publishing, 2014), pp. 149174.
    22. 22)
    23. 23)
    24. 24)
      • 24. Luu, K., Ricanek, K., Bui, T.D., Suen, C.Y.: ‘Age estimation using active appearance models and support vector machine regression’. Proc. IEEE Third Int. Conf. on Biometrics: Theory, Applications, and Systems, 2009, pp. 15.
    25. 25)
    26. 26)
      • 26. Guo, G., Mu, G., Fu, Y., Huang, T.S.: ‘Human age estimation using bio-inspired features’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2009, pp. 112119.
    27. 27)
      • 27. Wang, S., Xia, X., Qing, Z., Wang, H., Le, J.: ‘Aging face identification using biologically inspired features’. Proc. IEEE Int. Conf. on Signal Processing, Communication and Computing, 2013, pp. 15.
    28. 28)
      • 28. El Dib, M.Y., El-Saban, M.: ‘Human age estimation using enhanced bio-Inspired features’. Proc. 17th IEEE Int. Conf. on Image Processing, 2010, pp. 15891592.
    29. 29)
      • 29. Hong, L., Wen, D., Fang, C., Ding, X.: ‘A new biologically inspired active appearance model for face age estimation by using local ordinal ranking’. Proc. ACM Fifth Int. Conf. on Internet Multimedia Computing and Service, 2013, pp. 327330.
    30. 30)
      • 30. Suo, J., Wu, T., Zhu, S., Shan, S., Chen, X., Gao, W.: ‘Design sparse features for age estimation using hierarchical face model’. Proc. Eighth IEEE Int. Conf. on Automatic Face & Gesture Recognition, 2008, pp. 16.
    31. 31)
      • 31. Han, H., Otto, C., Jain, A.K.: ‘Age estimation from face images: human vs. machine performance’. Proc. Int. Conf. on Biometrics, 2013, pp. 18.
    32. 32)
      • 32. Zhou, H., Miller, P.C., Zhang, J.: ‘Age classification using radon transform and entropy based scaling SVM’. Proc. BMVC, 2011, pp. 112.
    33. 33)
    34. 34)
      • 34. Li, C., Liu, Q., Liu, J., Lu, H.: ‘Learning ordinal discriminative features for age estimation’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2012, pp. 25702577.
    35. 35)
      • 35. Chang, K.Y., Chen, C.S., Hung, Y.P.: ‘Ordinal hyperplanes ranker with cost sensitivities for age estimation’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2011, pp. 585592.
    36. 36)
    37. 37)
    38. 38)
      • 38. Zheng, Y., Yao, H., Zhang, Y., Xu, P.: ‘Age classification based on back-propagation network’. Proc. Fifth Int. Conf. on Internet Multimedia Computing and Service, 2013, pp. 319322.
    39. 39)
      • 39. Yin, C., Geng, X.: ‘Facial age estimation by conditional probability neural network’. Proc. Pattern Recognition, 2012, pp. 243250.
    40. 40)
      • 40. Thukral, P., Mitra, K., Chellappa, R.: ‘A hierarchical approach for human age estimation’. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, 2012, pp. 15291532.
    41. 41)
      • 41. Wu, T., Chellappa, R.: ‘Age invariant face verification with relative craniofacial growth model’. Proc. Computer Vision – ECCV, 2012, pp. 5871.
    42. 42)
      • 42. Akhtar, Z., Rattani, A., Hadid, A., Tistarelli, M.: ‘Face recognition under ageing effect: a comparative analysis’. Proc. Image Analysis and Processing – ICIAP, 2013, pp. 309318.
    43. 43)
      • 43. Otto, C., Han, H., Jain, A.: ‘How does aging affect facial components?’. Proc. Computer Vision – ECCV, Workshops and Demonstrations, 2012, pp. 189198.
    44. 44)
      • 44. Poh, N., Kittler, J., Chan, C.H., Pandit, M.: ‘An analysis of biometric performance change over time: a multimodal perspective’, in Fairhurst, M. (Ed.): ‘Age factors in biometric processing’ (IET, UK, 2013), pp. 185202.
    45. 45)
    46. 46)
      • 46. Tistarelli, M., Yadav, D., Vatsa, M., Singh, R.: ‘Short-and long-time ageing effects in face recognition’, in Fairhurst, M. (Ed.): ‘Age factors in biometric processing’ (IET, UK, 2013), pp. 253276.
    47. 47)
    48. 48)
    49. 49)
    50. 50)
      • 50. Juefei-Xu, F., Luu, K., Savvides, M., Bui, T.D., Suen, C.Y.: ‘Investigating age invariant face recognition based on periocular biometrics’. Proc. IEEE Int. Joint Conf. on Biometrics, 2011, pp. 17.
    51. 51)
      • 51. Yadav, D., Vatsa, M., Singh, R., Tistarelli, M.: ‘Bacteria foraging fusion for face recognition across age progression’. Computer Vision and Pattern Recognition Workshops, 2013, pp. 173179.
    52. 52)
      • 52. Singh, R., Vatsa, M., Noore, A., Singh, S.K.: ‘Age transformation for improving face recognition performance’. Proc. Pattern Recognition and Machine Intelligence, 2007, pp. 576583.
    53. 53)
    54. 54)
      • 54. Ali, A.S.O., Malik, A.S., Aziz, A.: ‘A geometrical approach for age-invariant face recognition’. Proc. Advances in Visual Informatics, 2013, pp. 8196.
    55. 55)
    56. 56)
    57. 57)
    58. 58)
      • 58. Patterson, E., Sethuram, A., Albert, M., Ricanek, K.: ‘Comparison of synthetic face aging to age progression by forensic sketch artist’. Proc. IASTED Int. Conf. on Visualization, Imaging, and Image Processing, 2007, pp. 247252.
    59. 59)
      • 59. Ricanek, K.Jr., Mahalingam, G., Albert, A.M., Bruegge, R.W.V.: ‘Human face ageing: a perspective analysis from anthropometry and biometrics’, in Fairhurst, M. (Ed.): ‘Age factors in biometric processing’ (IET, UK, 2013), pp. 93116.
    60. 60)
      • 60. Ricanek, K., Sethuram, A., Patterson, E.K., Albert, A.M., Boone, E.J.: ‘Craniofacial aging’, Wiley Handb. Sci. Technol. Homeland Secur., 2008, 3, (7), pp. 127.
    61. 61)
    62. 62)
      • 62. Kemelmacher-Shlizerman, I., Suwajanakorn, S., Seitz, S.M.: ‘Illumination-aware age progression’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 33343341.
    63. 63)
    64. 64)
      • 64. Tsai, M.H., Liao, Y.K., Lin, I.C.: ‘Human face aging with guided prediction and detail synthesis’, Multimedia Tools Appl., 2013, 72, (1), pp. 124.
    65. 65)
    66. 66)
    67. 67)
    68. 68)
      • 68. Maronidis, A., Lanitis, A.: ‘Facial age simulation using age-specific 3D models and recursive PCA’. Proc. VISAPP, 2013, no. 1, pp. 663668.
    69. 69)
    70. 70)
    71. 71)
      • 71. Shen, C.T., Huang, F., Lu, W., Shih, S.W., Liao, H.Y.M.: ‘3D age progression prediction in children's faces with a small exemplar-image set’, J. Inf. Sci. Eng., 2014, 30, pp. 11311148.
    72. 72)
      • 72. Ramanathan, N., Chellappa, R.: ‘Modeling age progression in young faces’. Proc. Computer Vision and Pattern Recognition Conf., 2006, vol. 1, pp. 387394.
    73. 73)
      • 73. Ni, B., Song, Z., Yan, S.: ‘Web image mining towards universal age estimator’. Proc. 17th ACM Int. Conf. on Multimedia, 2009, pp. 8594.
    74. 74)
      • 74. Zhou, S.K., Georgescu, B., Zhou, X.S., Comaniciu, D.: ‘Image based regression using boosting method’. Proc. Tenth IEEE Int. Conf. on Computer Vision, 2005, pp. 541548.
    75. 75)
      • 75. Xiao, B., Yang, X., Xu, Y., Zha, H.: ‘Learning distance metric for regression by semidefinite programming with application to human age estimation’. Proc. 17th ACM Int. Conf. on Multimedia, 2009, pp. 451460.
    76. 76)
      • 76. Günay, A., Nabiyev, V.V.: ‘Age estimation based on local radon features of facial images’. Proc. Computer and Information Sciences III, 2013, pp. 183190.
    77. 77)
      • 77. Yan, S., Wang, H., Tang, X., Huang, T.S.: ‘Learning auto-structured regressor from uncertain nonnegative labels’. Proc. IEEE Eleventh Int. Conf. on Computer Vision, 2007, pp. 18.
    78. 78)
      • 78. Yan, S., Wang, H., Huang, T.S., Yang, Q., Tang, X.: ‘Ranking with uncertain labels’. Proc. IEEE Int. Conf. on Multimedia and Expo, 2007, pp. 9699.
    79. 79)
    80. 80)
      • 80. Ylioinas, J., Hadid, A., Hong, X., Pietikäinen, M.: ‘Age estimation using local binary pattern kernel density estimate’. Proc. Image Analysis and Processing, 2013, pp. 141150.
    81. 81)
      • 81. Kilinc, M., Akgul, Y.S.: ‘Automatic human age estimation using overlapped age groups’. Proc. Computer Vision, Imaging and Computer Graphics Theory and Application, 2013, pp. 313325.
    82. 82)
      • 82. Guo, G., Fu, Y., Dyer, C.R., Huang, T.S.: ‘A probabilistic fusion approach to human age prediction’. Proc. IEEE Computer Vision and Pattern Recognition Workshops, 2008, pp. 16.
    83. 83)
      • 83. Liang, Y., Wang, X., Zhang, L., Wang, Z.: ‘A hierarchical framework for facial age estimation’, Math. Prob. Eng., 2014, 2014, pp. 18.
    84. 84)
      • 84. Yan, S., Zhou, X., Liu, M., Hasegawa-Johnson, M., Huang, T.S.: ‘Regression from patch-kernel’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2008, pp. 18.
    85. 85)
      • 85. Zhang, L., Wang, X., Liang, Y., Xie, L.: ‘A new method for age estimation from facial images by hierarchical model’. Proc. Second Int. Conf. on Innovative Computing and Cloud Computing, 2013, p. 88.
    86. 86)
      • 86. Chen, K., Gong, S., Xiang, T., Loy, C.C.: ‘Cumulative attribute space for age and crowd density estimation’, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Portland, OR, 2013, pp. 24672474.
    87. 87)
    88. 88)
      • 88. Guo, G., Zhang, C.: ‘A study on cross-population age estimation’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 42574263.
    89. 89)
    90. 90)
      • 90. Dibeklioğlu, H., Gevers, T., Salah, A.A., Valenti, R.: ‘A smile can reveal your age: enabling facial dynamics in age estimation’. Proc. 20th ACM Int. Conf. on Multimedia, 2012, pp. 209218.
    91. 91)
      • 91. Dibeklioğlu, H., Salah, A.A., Gevers, T.: ‘Are you really smiling at me? Spontaneous versus posed enjoyment smiles’. Proc. Computer Vision – ECCV, 2012, pp. 525538.
    92. 92)
    93. 93)
      • 93. Lanitis, A.: ‘Age estimation based on head movements: a feasibility study’. Proc. Fourth Int. Symp. on Communications, Control and Signal Processing, 2010, pp. 16.
    94. 94)
      • 94. Gnanasivam, P., Muttan, D.S.: ‘Estimation of age through fingerprints using wavelet transform and singular value decomposition’, Int. J. Biometrics Bioinf., 2012, 6, (2), pp. 5867.
    95. 95)
      • 95. Xia, B., Amor, B.B., Daoudi, M., Drira, H.: ‘Can 3D shape of the face reveal your age?’. Proc. Int. Conf. on Computer Vision Theory and Applications, 2014, pp. 513.
    96. 96)
      • 96. Phillips, P.J., Flynn, P.J., Scruggs, T., et al: ‘Overview of the face recognition grand challenge’. Proc. Computer Vision and Pattern Recognition Conf., 2005, pp. 947954.
    97. 97)
    98. 98)
    99. 99)
      • 99. Santos, G., Proença, H.: ‘Periocular biometrics: an emerging technology for unconstrained scenarios’. Proc. IEEE Symp. on Computational Intelligence in Biometrics and Identity Management – CIBIM, 2013, pp. 1421.
    100. 100)
      • 100. ‘Aging Booth’. Available at http://www.piviandco.com/apps/agingbooth/, accessed November 2014.
    101. 101)
      • 101. ‘AgeMe’. Available at https://www.aprilage.com/ageme|, accessed November 2014.
    102. 102)
      • 102. Lanitis, A.: ‘Evaluating the performance of face-aging algorithms’. Proc. Eighth IEEE Int. Conf. on Automatic Face & Gesture Recognition, 2008, pp. 16.
    103. 103)
      • 103. Lanitis, A., Tsapatsoulis, N., Soteriou, K., Kuwahara, D., Morishima, S.: ‘FG2015 age progression evaluation’. Proc. of the IEEE Face and Gesture Recognition Conf. (FG2015), 2015.
    104. 104)
      • 104. Patterson, E., Simpson, D., Sethuram, A.: ‘Establishing a test set and initial comparisons for quantitatively evaluating synthetic age progression for adult aging’. Proc. IEEE Int. Joint Conf. on Biometrics, 2014, pp. 18.
    105. 105)
    106. 106)
      • 106. Guo, G., Mu, G., Fu, Y., Dyer, C., Huang, T.: ‘A study on automatic age estimation using a large database’. Proc. IEEE 12th Int. Conf. on Computer Vision, 2009, pp. 19861991.
    107. 107)
      • 107. Ueki, K., Hayashida, T., Kobayashi, T.: ‘Subspace-based age-group classification using facial images under various lighting conditions’. Proc. Seventh Int. Conf. on Automatic Face and Gesture Recognition, 2006, pp. 4348.
    108. 108)
    109. 109)
      • 109. Suo, J., Min, F., Zhu, S., Shan, S., Chen, X.: ‘A multi-resolution dynamic model for face aging simulation’. Proc. Computer Vision and Pattern Recognition Conf., 2007, pp. 18.
    110. 110)
      • 110. Bastanfard, A., Nik, M.A., Dehshibi, M.M.: ‘Iranian face database with age, pose and expression’. Proc. IEEE Int. Conf. on Machine Vision, 2007, pp. 5055.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2014.0053
Loading

Related content

content/journals/10.1049/iet-bmt.2014.0053
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address