Article Sidebar
Main Article Content
Age prediction has become an important Computer Vision task. Although this task requires the age of an individual to be predicted from a given face, research has shown that it is more intuitive and easier for humans to decide which of two individuals is older than to decide how old an individual is. This work follows this intuition to aid the age prediction of a face by exploiting the age information available from other faces. It goes further to explore the statistical relationships between facial features within age groups to compute age-group ranks for a given face. The resulting age-group rank is low-dimensional and age-discriminatory, thus improving age prediction accuracy when fed into an age predictor. Experiments on publicly available facial ageing datasets (FGnet, PAL, and Adience) reveal the effectiveness of the proposed age-group ranking model when used with traditional Machine learning algorithms as well as Deep Learning algorithms. Cross-dataset validation, a method of training and testing on entirely different datasets, was also employed to further investigate the effectiveness of this method.
Article Details
O. Agbo-Ajala and S. Viriri. Deeply learned classifiers for age and gender predictions of unfiltered faces. Scientific World Journal, 2020:1289408, 2020. https://doi.org/10.1155/2020/1289408. (Crossref)
J. D. Akinyemi. GWAgeER; A GroupWise Age-Ranking Approach to Age Estimation from Still Facial Image. Master's thesis, University of Ibadan, Ibadan, 2014. 161 pages. https://doi.org/10.13140/RG.2.1.2495.1763, https://ibadan.academia.edu/AkinyemiDamilola/Theses.
J. D. Akinyemi and O. F. W. Onifade. An ethnic-specific age group ranking approach to facial age estimation using raw pixel features. In: Proc. 2016 IEEE Symposium on Technologies for Homeland Security (HST), pp. 1-6. IEEE, Waltham, MA, USA, 10-11 May 2016. https://doi.org/10.1109/THS.2016.7819737. (Crossref)
J. D. Akinyemi and O. F. W. Onifade. A computational face alignment method for improved facial age estimation. In: Proc. 2019 15th International Conference on Electronics, Computer and Computation (ICECCO), pp. 1-6. IEEE, Abuja, Nigeria, 12 2019. https://doi.org/10.1109/ICECCO48375.2019.9043246. (Crossref)
J. D. Akinyemi and O. F. W. Onifade. Facial age estimation using compact facial features. In: Computer Vision and Graphics: Proc. International Conference on Computer Vision and Graphics (ICCVG) 2020, vol. 12334 of Lecture Notes in Computer Science, pp. 1-12. Springer International Publishing, Warsaw, Poland, Sep 14-16 2020. https://doi.org/10.1007/978-3-030-59006-2_1. (Crossref)
F. Alnajar and J. Alvarez. Expression-invariant age estimation. In: Proc. 25th British Machine Vision Conference (BMVC) 2014, pp. 28.1-28.11. Nottingham, UK, 1-5 Sep 2014. (doi inoperative). https://doi.org/10.5244/C.28.14, https://bmva-archive.org.uk/bmvc/2014/papers/paper081/index.html. (Crossref)
K.-Y. Chang and C.-S. Chen. A learning framework for age rank estimation based on face images with scattering transform. IEEE Transactions on Image Processing, 24(3):785-798, 2015. https://doi.org/10.1109/TIP.2014.2387379. (Crossref)
K.-Y. Chang, C.-S. Chen, and Y.-P. Hung. A ranking approach for human ages estimation based on face images. In: Proc. 2010 20th International Conference on Pattern Recognition (ICPR), pp. 3396-3399. Istanbul, Turkey, 23-26 Aug 2010. https://doi.org/10.1109/ICPR.2010.829. (Crossref)
K.-Y. Chang, C.-S. Chen, and Y. P. Hung. Ordinal hyperplanes ranker with cost sensitivities for age estimation. In: Proc. 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 585-592. Colorado Springs, CO, USA, 20-25 Jun 2011. https://doi.org/10.1109/CVPR.2011.5995437. (Crossref)
S. Chen, C. Zhang, M. Dong, J. Le, and M. Rao. Using Ranking-CNN for age estimation. In: Proc. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 742-751. Honolulu, HI, USA, 21-26 Jul 2017. https://doi.org/10.1109/CVPR.2017.86. (Crossref)
F. Chollet. Xception: Deep learning with depthwise separable convolutions. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800-1807. Honolulu, HI, USA, 21-26 Jul 2017. https://doi.org/10.1109/CVPR.2017.195. (Crossref)
T. F. Cootes, G. Rigoll, E. Granum, J. L. Crowley, S. Marcel, et al. Face and Gesture Recognition Working group, 2002. http://www-prima.inrialpes.fr/FGnet/, FGnet - Project IST-2000-26434. Original URL is not operative, copy can be accessed at http://crowley-coutaz.fr/FGnet/html/home.html.
R. Diaz and A. Marathe. Soft labels for ordinal regression. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4733-4742. Long Beach, CA, USA, 15-20 Jun 2019. https://doi.org/10.1109/CVPR.2019.00487. (Crossref)
M. Y. E. Dib and H. M. Onsi. Human age estimation framework using different facial parts. Egyptian Informatics Journal, 12(1):53-59, 2011. https://doi.org/10.1016/j.eij.2011.02.002. (Crossref)
F. Dornaika, I. Arganda-Carreras, and C. Belver. Age estimation in facial images through transfer learning. Machine Vision and Applications, 30(1):177-187, 2019. https://doi.org/10.1007/s00138-018-0976-1. (Crossref)
M. Duan, K. Li, and K. Li. An ensemble cnn2elm for age estimation. IEEE Transactions on Information Forensics and Security, 13(3):758-772, 2018. https://doi.org/10.1109/TIFS.2017.2766583. (Crossref)
E. Eidinger, R. Enbar, and T. Hassner. Age and gender estimation of unfiltered faces. IEEE Transactions on Information Forensics and Security, 9(12):2170-2179, 2014. https://doi.org/10.1109/TIFS.2014.2359646. (Crossref)
Y. Fu, G. Guo, and T. S. Huang. Age synthesis and estimation via faces: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11):1955-1976, 2010. https://doi.org/10.1109/TPAMI.2010.36. (Crossref)
X. Geng, Z.-H. Zhou, Y. Zhang, G. Li, and H. Dai. Learning from facial aging patterns for automatic age estimation. In: K. Nahrstedt, M. Turk, Y. Rui, W. Klas, and K. Mayer-Patel, eds., Proc. MM '06: Proc. 14th ACM International Conference on Multimedia, pp. 307-316. ACM, Santa Barbara, CA USA, 23-27 Oct 2006. https://doi.org/10.1145/1180639.1180711. (Crossref)
P. A. George and G. J. Hole. Factors influencing the accuracy of age-estimates of unfamiliar faces. Perception, 24(9):1059-1073, 1995. https://doi.org/10.1068/p241059. (Crossref)
G. Guo, G. Mu, Y. Fu, and T. S. Huang. Human age estimation using bio-inspired features. In: Proc. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, pp. 112-119. Miami, FL, USA, 20-25 Jun 2009. https://doi.org/10.1109/CVPRW.2009.5206681. (Crossref)
T. Hassner. The OUI-Adinece Face Image project. The Open University of Israel. https://talhassner.github.io/home/projects/Adience/Adience-data.html, [Accessed May 2024].
C. Kong, Q. Luo, and G. Chen. Learning deep contrastive network for facial age estimation. In: Proc. International Joint Conference on Neural Networks (IJCNN). IEEE, Padua, Italy, 18-23 Jul 2022. https://doi.org/10.1109/IJCNN55064.2022.9892308. (Crossref)
Y. H. Kwon and N. da Vitoria Lobo. Age classification from facial images. In: Proc. IEEE International Conference on Computer Vision and Pattern Recognition (ICCVPR), p. 762–767. Seattle, WA, USA, 21-23 Jun 1994. https://doi.org/10.1109/CVPR.1994.323894. (Crossref)
A. Lanitis. On the significance of different facial parts for automatic age estimation. In: Proc. International Conference on Digital Signal Processing (DSP), vol. 2, pp. 1027-1030. Santorini, Greece, 01-03 Jul 2002. https://doi.org/10.1109/ICDSP.2002.1028265. (Crossref)
G. Levi and T. Hassner. Age and gender classification using convolutional neural networks. In: Proc. 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 34-42. Boston, MA, USA, 07-12 Jun 2015. https://doi.org/10.1109/CVPRW.2015.7301352. (Crossref)
W. Li, J. Lu, J. Feng, C. Xu, J. Zhou, et al. BridgeNet: A continuity-aware probabilistic network for age estimation. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1145-1154. Long Beach, CA, USA, 15-20 Jun 2019. https://doi.org/10.1109/CVPR.2019.00124. (Crossref)
H. Liu, J. Lu, J. Feng, and J. Zhou. Ordinal deep feature learning for facial age estimation. In: Proc. 12th IEEE International Conference on Automatic Face and Gesture Recognition, (FG), pp. 157-164, 30 May - 03 Jun 2017. https://doi.org/10.1109/FG.2017.28. (Crossref)
H. Liu, J. Lu, J. Feng, and J. Zhou. Ordinal deep learning for facial age estimation. IEEE Transactions on Circuits and Systems for Video Technology, 29(2):486-501, 2019. https://doi.org/10.1109/TCSVT.2017.2782709. (Crossref)
K. H. Liu, S. Yan, and C.-C. J. Kuo. Age estimation via grouping and decision fusion. IEEE Transactions on Information Forensics and Security, 10(11):2408-2423, 2015. https://doi.org/10.1109/TIFS.2015.2462732. (Crossref)
Z. Lou, F. Alnajar, J. M. Alvarez, N. Hu, and T. Gevers. Expression-invariant age estimation using structured learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2):365-375, 2018. https://doi.org/10.1109/TPAMI.2017.2679739. (Crossref)
M. Minear and D. C. Park. A lifespan database of adult facial stimuli. Behavior Research Methods, Instruments, and Computers, 36(4):630-633, 2004. https://doi.org/10.3758/BF03206543. (Crossref)
S. H. Nam, Y. H. Kim, N. Q. Truong, J. Choi, and K. R. Park. Age estimation by super-resolution reconstruction based on adversarial networks. IEEE Access, 8:17103-17120, 2020. https://doi.org/10.1109/ACCESS.2020.2967800. (Crossref)
T. Ojala, M. Pietikäinen, and T. Mäenpää. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971-987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623. (Crossref)
H. Pan, H. Han, S. Shan, and X. Chen. Mean-variance loss for deep age estimation from a face. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5285-5294. Salt Lake City, UT, USA, 18-23 Jun 2018. https://doi.org/10.1109/CVPR.2018.00554. (Crossref)
O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. In: Proc. 26th British Machine Vision Conference (BMVC), pp. 41.1-41.12. Swansea, UK, 7-10 Sep 2015. (doi inoperative). https://doi.org/10.5244/c.29.41, https://bmva-archive.org.uk/bmvc/2015/papers/paper041/. (Crossref)
R. Ranjan, S. Sankaranarayanan, C. D. Castillo, and R. Chellappa. An all-in-one convolutional neural network for face analysis. In: Proc. 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 17-24. Washington, DC, USA, 30 May - 03 Jun 2017. https://doi.org/10.1109/FG.2017.137. (Crossref)
G. Rhodes. Lateralized processes in face recognition. British Journal of Psychology, 76(2):249-271, 1985. https://doi.org/10.1111/j.2044-8295.1985.tb01949.x. (Crossref)
P. Rodríguez, G. Cucurull, J. M. Gonfaus, F. X. Roca, and J. Gonzàlez. Age and gender recognition in the wild with deep attention. Pattern Recognition, 72:563-571, 2017. https://doi.org/10.1016/j.patcog.2017.06.028. (Crossref)
R. Rothe, R. Timofte, and L. V. Gool. Deep expectation of real and apparent age from a single image without facial landmarks. International Journal of Computer Vision, 126(2):144-157, 2018. https://doi.org/10.1007/s11263-016-0940-3. (Crossref)
W. Samek, A. Binder, S. Lapuschkin, and K.-R. Müller. Understanding and comparing deep neural networks for age and gender classification. In: Proc. 2017 IEEE International Conference on Computer Vision Workshops, (ICCVW), pp. 1629-1638. Venice, Italy, 22-29 Oct 2017. https://doi.org/10.1109/ICCVW.2017.191. (Crossref)
W. Shen, Y. Guo, Y. Wang, K. Zhao, B. Wang, et al. Deep regression forests for age estimation. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2304-2313. Salt Lake City, UT, USA, 18-23 Jun 2018. https://doi.org/10.1109/CVPR.2018.00245. (Crossref)
W. Shen, Y. Guo, Y. Wang, K. Zhao, B. Wang, et al. Deep differentiable random forests for age estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(2):404-419, 2019. https://doi.org/10.1109/tpami.2019.2937294. (Crossref)
C. Shi, S. Zhao, K. Zhang, Y. Wang, and L. Liang. Face-based age estimation using improved swin transformer with attention-based convolution. Frontiers in Neuroscience, 17, 2023. https://doi.org/10.3389/fnins.2023.1136934. (Crossref)
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In: Proc. 3rd International Conference on Learning Representations (ICLR). San Diego, CA, USA, 7-9 May 2015. Published only on arXiv. http://arxiv.org/abs/1409.1556.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2818-2826. Las Vegas, NV, USA, 27-30 Jun 2016. https://doi.org/10.1109/CVPR.2016.308. (Crossref)
Z. Tan, J. Wan, Z. Lei, R. Zhi, G. Guo, et al. Efficient group-n encoding and decoding for facial age estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(11):2610-2623, 2018. https://doi.org/10.1109/TPAMI.2017.2779808. (Crossref)
J. C. Xie and C. M. Pun. Deep and ordinal ensemble learning for human age estimation from facial images. IEEE Transactions on Information Forensics and Security, 15(8):2361-2374, 2020. https://doi.org/10.1109/TIFS.2020.2965298. (Crossref)
H.-F. Yang, B.-Y. Lin, K.-Y. Chang, and C.-S. Chen. Automatic age estimation from face images via deep ranking. In: Proc. 26th British Machine Vision Conference (BMVC), pp. 55.1-55.11. Swansea, UK, 7-10 Sep 2015. (doi inoperative). https://doi.org/10.5244/C.29.55, https://bmva-archive.org.uk/bmvc/2015/papers/paper055/. (Crossref)
C. Zhang and G. Guo. Age estimation with expression changes using multiple aging subspaces. In: Proc. IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1-6. Arlington, VA, USA, 29 Sep - 02 Oct 2013. https://doi.org/10.1109/BTAS.2013.6712720. (Crossref)
C. Zhang, S. Liu, X. Xu, and C. Zhu. C3AE: Exploring the limits of compact model for age estimation. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12579-12588. Long Beach, CA, USA, 15-20 Jun 2019. https://doi.org/10.1109/CVPR.2019.01287. (Crossref)
Downloads
- Joseph D. Akinyemi, Akinkunle A. Akinola, Olajumoke O. Adekunle, Taiwo O. Adetiloye, Emmanuel J. Dansu, Lung and colon cancer detection from CT images using Deep Learning , Machine Graphics and Vision: Vol. 32 No. 1 (2023)