access icon free Automatic age estimation from facial profile view

In recent years, automatic facial age estimation has gained popularity due to its numerous applications. Much work has been done on frontal images and lately, minimal estimation errors have been achieved on most of the benchmark databases. However, in reality, images obtained in unconstrained environments are not always frontal. For instance, when conducting a demographic study or crowd analysis, one may get profile images of the face. To the best of our knowledge, no attempt has been made to estimate ages from the side-view of face images. Here the authors exploit this by using a pretrained deep residual neural network to extract features, and then utilise a sparse partial least-squares regression approach to estimate ages. Despite having less information as compared with frontal images, the results show that the extracted deep features achieve a promising performance.

Inspec keywords: age issues; face recognition; visual databases; regression analysis; feature extraction

Other keywords: pretrained deep residual neural network; feature extraction; frontal images; benchmark databases; crowd analysis; sparse partial least-squares regression approach; demographic study; facial profile view; automatic facial age estimation

Subjects: Image recognition; Economic, social and political aspects of computing; Other topics in statistics; Other topics in statistics; Computer vision and image processing techniques

References

    1. 1)
      • 37. Russakovsky, O., Deng, J., Su, H., et al: ‘Imagenet large scale visual recognition challenge’, Int. J. Comput. Vis., 2015, 115, (3), pp. 211252.
    2. 2)
      • 11. Liu, T., Wan, J., Yu, T., et al: ‘Age estimation based on multi-region convolutional neural network’. Chinese Conf. on Biometric Recognition, 2016, pp. 186194.
    3. 3)
      • 35. Athiwaratkun, B., Kang, K.: ‘Feature representation in convolutional neural networks’, arXiv Prepr. arXiv1507.02313, 2015.
    4. 4)
      • 17. Geng, X., Zhou, Z.-H., Zhang, Y., et al: ‘Learning from facial aging patterns for automatic age estimation’. Proc. 14th Annu. ACM Int. Conf. on Multimedia, 2006, p. 307.
    5. 5)
      • 44. Phillips, P.J., Wechsler, H., Huang, J., et al: ‘The FERET database and evaluation procedure for face-recognition algorithms’, Image Vis. Comput., 1998, 16, (5), pp. 295306.
    6. 6)
      • 29. Yosinski, J., Clune, J., Bengio, Y., et al: ‘How transferable are features in deep neural networks?’. Advances in Neural Information Processing Systems, 2014, pp. 33203328.
    7. 7)
      • 23. Zhu, Y., Li, Y., Mu, G., et al: ‘A study on apparent age estimation’. Proc. IEEE Int. Conf. on Computer Vision Workshops, 2015, pp. 2531.
    8. 8)
      • 4. Choi, S.E., Lee, Y.J., Lee, S.J., et al: ‘Age estimation using a hierarchical classifier based on global and local facial features’, Pattern Recognit., 2011, 44, (6), pp. 12621281.
    9. 9)
      • 38. Chun, H., Keleş, S.: ‘Sparse partial least squares regression for simultaneous dimension reduction and variable selection’, J. R. Stat. Soc. Ser. B: Stat. Methodol., 2010, 72, (1), pp. 325.
    10. 10)
      • 22. Liu, X., Li, S., Kan, M., et al: ‘Agenet: deeply learned regressor and classifier for robust apparent age estimation’. Proc. IEEE Int. Conf. on Computer Vision Workshops, 2015, pp. 1624.
    11. 11)
      • 13. Ramanathan, N., Chellappa, R.: ‘Modeling age progression in young faces’. 2006 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2006, vol. 1, pp. 387394.
    12. 12)
      • 36. Srivastava, R.K., Greff, K., Schmidhuber, J.: ‘Highway networks’, arXiv Prepr. arXiv1505.00387, 2015.
    13. 13)
      • 10. Wang, X., Guo, R., Kambhamettu, C.: ‘Deeply-learned feature for age estimation’. 2015 IEEE Winter Conf. on Applications of Computer Vision, 2015, pp. 534541.
    14. 14)
      • 40. Huang, T.S.: ‘Human age estimation using bio-inspired features’. 2009 IEEE Conf. on Computer Vision and Pattern Recognition, June 2009, pp. 112119.
    15. 15)
      • 3. Niu, Z., Zhou, M., Wang, L., et al: ‘Ordinal regression with multiple output CNN for age estimation’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2016, pp. 49204928.
    16. 16)
      • 1. Kaur, M., Garg, R.K., Singla, S.: ‘Analysis of facial soft tissue changes with aging and their effects on facial morphology: a forensic perspective’, Egypt. J. Forensic Sci., 2015, 5, (2), pp. 4656.
    17. 17)
      • 15. Gunay, A., Nabiyev, V.V.: ‘Automatic detection of anthropometric features from facial images’. 2007 IEEE 15th Signal Processing and Communications Applications, 2007, pp. 14.
    18. 18)
      • 18. Yan, S., Wang, H., Tang, X., et al: ‘Learning auto-structured regressor from uncertain nonnegative labels’. 2007 IEEE 11th Int. Conf. on Computer Vision, 2007, pp. 18.
    19. 19)
      • 2. Fu, Y., Guo, G., Huang, T.: ‘Age synthesis and estimation via faces: a survey’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (11), pp. 19551976.
    20. 20)
      • 33. Babenko, A., Slesarev, A., Chigorin, A., et al: ‘Neural codes for image retrieval’. European Conf. on Computer Vision, 2014, pp. 584599.
    21. 21)
      • 39. De Jong, S.: ‘SIMPLS: an alternative approach to partial least squares regression’, Chemom. Intell. Lab. Syst., 1993, 18, (3), pp. 251263.
    22. 22)
      • 14. Shen, C.T., Huang, F., Lu, W.H., et al: ‘3D Age Progression Prediction in Children's Faces with a Small Exemplar-Image Set’, Journal of Information Science and Engineering, 2014, 30, (4), pp. 11311148.
    23. 23)
      • 21. Rothe, R., Timofte, R., Van Gool, L.: ‘Dex: deep expectation of apparent age from a single image’. Proc. IEEE Int. Conf. on Computer Vision Workshops, 2015, pp. 1015.
    24. 24)
      • 16. Lanitis, A., Taylor, C., Cootes, T.: ‘Toward automatic simulation of aging effects on face images’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (4), pp. 442455.
    25. 25)
      • 6. Eidinger, E., Enbar, R., Hassner, T.: ‘Age and gender estimation of unfiltered faces’, IEEE Trans. Inf. Forensics Secur., 2014, 9, (12), pp. 21702179.
    26. 26)
      • 47. Szegedy, C., Liu, W., Jia, Y., et al: ‘Going deeper with convolutions’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2015, pp. 19.
    27. 27)
      • 19. Fu, Y., Xu, Y., Huang, T.S.: ‘Estimating human age by manifold analysis of face pictures and regression on aging features’. 2007 IEEE Int. Conf. on Multimedia and Expo, 2007, pp. 13831386.
    28. 28)
      • 26. He, K., Zhang, X., Ren, S., et al: ‘Deep residual learning for image recognition’, arXiv Prepr. arXiv1512.03385, 2015.
    29. 29)
      • 46. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’, arXiv Prepr. arXiv1409.1556, 2014.
    30. 30)
      • 31. Sermanet, P., Eigen, D., Zhang, X., et al: ‘Overfeat: integrated recognition, localization and detection using convolutional networks’, arXiv Prepr. arXiv1312.6229, 2013.
    31. 31)
      • 45. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘Imagenet classification with deep convolutional neural networks’. Advances in Neural Information Processing Systems, 2012, pp. 10971105.
    32. 32)
      • 20. Guo, G., Mu, G., Fu, Y., et al: ‘Human age estimation using bio-inspired features’. 2009 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2009), 2009, pp. 112119.
    33. 33)
      • 42. Vieira, T.F., Bottino, A., Laurentini, A., et al: ‘Detecting siblings in image pairs’, Vis. Comput., 2014, 30, (12), pp. 13331345.
    34. 34)
      • 34. Shin, H.-C., Roth, H.R., Gao, M., et al: ‘Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning’, IEEE Trans. Med. Imaging, 2016, 35, (5), pp. 12851298.
    35. 35)
      • 24. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, (7553), pp. 436444.
    36. 36)
      • 12. Kwon, Y.H., Lobo, V.: ‘Age classification from facial images’. Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR-94), 1994, pp. 762767.
    37. 37)
      • 41. Panis, G., Lanitis, A., Tsapatsoulis, N., et al: ‘Overview of research on facial ageing using the FG-NET ageing database’, IET Biometrics, 2016, 5, (2), pp. 3746.
    38. 38)
      • 5. Fernandez, C., Huerta, I., Prati, A.: ‘A comparative evaluation of regression learning algorithms for facial age estimation’. FFER Conjunction with ICPR, 2014.
    39. 39)
      • 8. Weng, R., Lu, J., Yang, G., et al: ‘Multi-feature ordinal ranking for facial age estimation’. 2013 Tenth IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition (FG), 2013, pp. 16.
    40. 40)
      • 28. Oquab, M., Bottou, L., Laptev, I., et al: ‘Learning and transferring mid-level image representations using convolutional neural networks’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 17171724.
    41. 41)
      • 32. Karpathy, A., Toderici, G., Shetty, S., et al: ‘Large-scale video classification with convolutional neural networks’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2014, pp. 17251732.
    42. 42)
      • 7. Kim, J., Han, D., Sohn, S., et al: ‘Facial age estimation via extended curvature Gabor filter’. 2015 IEEE Int. Conf. on Image Processing (ICIP), 2015, pp. 11651169.
    43. 43)
      • 30. Razavian, A.S., Azizpour, H., Sullivan, J., et al: ‘CNN features off-the-shelf: an astounding baseline for recognition’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2014, pp. 806813.
    44. 44)
      • 9. Han, H., Otto, C., Jain, A.K., et al: ‘Age Estimation from Face Images: Human vs. Machine Performance’, 2013.
    45. 45)
      • 25. Long, J., Shelhamer, E., Darrell, T.: ‘Fully convolutional networks for semantic segmentation’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2015, pp. 34313440.
    46. 46)
      • 27. Azizpour, H., Sharif Razavian, A., Sullivan, J., et al: ‘From generic to specific deep representations for visual recognition’. Proc. IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2015, pp. 3645.
    47. 47)
      • 43. Dalrymple, K.A., Gomez, J., Duchaine, B.: ‘The Dartmouth database of children's faces: acquisition and validation of a new face stimulus set’, PLoS ONE, 2013, 8, (11), pp. 17.
    48. 48)
      • 48. El Dib, M.Y., El-Saban, M.: ‘Human age estimation using enhanced bio-inspired features (EBIF)’. 2010 IEEE Int. Conf. on Image Processing, September 2010, pp. 15891592.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2016.0486
Loading

Related content

content/journals/10.1049/iet-cvi.2016.0486
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading