NEWS

AGE ESTIMATION USING SPECIFIC DOMAIN TRANSFER LEARNING


(Received: 19-Oct-2019, Revised: 11-Dec-2019 , Accepted: 29-Dec-2019)
Nowadays, the engagement of deep neural networks in computer vision increases the ability to achieve higher accuracy in many learning tasks, such as face recognition and detection. However, the automatic estimation of human age is still considered as the most challenging facial task that demands extra efforts to obtain an accepted accuracy for real application. In this paper, we attempt to obtain a satisfied model that overcomes the overfitting problem, by fine-tuning CNN model which was pre-trained on face recognition task to estimate the real age. To make the model more robust, we evaluated the model for real age estimation on two types of datasets: on the constrained FG_NET dataset, we achieved 3.446 of MAE, while on the unconstrained UTKFace dataset, we achieved 4.867 of MAE. The experimental results of our approach outperform other state-of-the-art age estimation models on the benchmark datasets. We also fine-tuned the model for age group classification task on Adience dataset and our model achieved an accuracy of 61.4%.

[1] A. K. Jain, S. C. Dass and K. Nandakumar, "Soft Biometric Traits for Personal Recognition Systems," Proceedings of International Conference on Biometric Authentication, Berlin, Heidelberg, vol. 3072, pp. 731–738, 2004.

[2] Y. Fu, G. Guo and T. S. Huang, "Age Synthesis and Estimation via Faces: A Survey," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 11, pp. 1955–1976, 2010.

[3] Y. LeCun, Y. Bengio and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, May 2015.

[4] S. J. Pan and Q. Yang, "A Survey on Transfer Learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, 2010.

[5] O. M. Parkhi, A. Vedaldi and A. Zisserman, "Deep Face Recognition," British Machine Vision Conference, 2015.

[6] T. Zheng, W. Deng and J. Hu, "Age Estimation Guided Convolutional Neural Network for Age-Invariant Face Recognition," Proc. of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 503–511, 2017.

[7] T. F. Cootes, G. J. Edwards and C. J. Taylor, "Active Appearance Models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 681–685, 2001.

[8] G. Guo, T. S. Huang, Y. Fu and T. S. Huang, "Human Age Estimation Using Bio-inspired Features," Proc. of IEEE Conference on Computer Vision and Pattern Recognition, USA, pp. 112–119, 2009.

[9] T. Ahonen, A. Hadid and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037–2041, 2006.

[10] A. S. Al-Shannaq and L. A. Elrefaei, "Comprehensive Analysis of the Literature for Age Estimation From Facial Images," IEEE Access, vol. 7, pp. 93229–93249, 2019.

[11] Y. H. Kwon and N. da V. Lobo, "Age Classification from Facial Images," Comput. Vis. Image Underst., vol. 74, no. 1, pp. 1–21, Apr. 1999. 

[12] A. Gunay and V. V Nabiyev, "Automatic Detection of Anthropometric Features from Facial Images," Proc. of the 15th IEEE Signal Processing and Communications Applications, pp. 1–4, 2007.

[13] M. M. Dehshibi and A. Bastanfard, "A New Algorithm for Age Rcognition from Facial Images," Signal Process., vol. 90, no. 8, pp. 2431–2444, Aug. 2010.

[14] G. Guo and G. Mu, "A Framework for Joint Estimation of Age, Gender and Ethnicity on a Large Database," Image Vis. Comput., vol. 32, no. 10, pp. 761–770, 2014.

[15] G. Guo and G. Mu, "Simultaneous Dimensionality Reduction and Human Age Estimation via Kernel Partial Least Squares Regression," CVPR 2011, pp. 657–664, 2011.

[16] H. Han, C. Otto and A. K. Jain, "Age Estimation from Face Images: Human vs. Machine Performance," Proc. of the 2013 Int. Conf. Biom. (ICB), pp. 1–8, 2013.

[17] X. Geng, Z. Zhou, S. Member, K. Smith-miles and S. Member, "Automatic Age Estimation Based on Facial Aging Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2234–2240, 2007.

[18] X. Geng, Z.-H. Zhou, Y. Zhang, G. Li and H. Dai, "Learning from Facial Aging Patterns for Automatic Age Estimation," Proceedings of the 14th ACM International Conference on Multimedia, pp. 307–316, 2006.

[19] Y. Fu and T. S. Huang, "Human Age Estimation with Regression on Discriminative Aging Manifold," IEEE Trans. Multimed., vol. 10, no. 4, pp. 578–584, 2008.

[20] G. Guo, Y. Fu, C. R. Dyer and T. S. Huang, "Image-based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression," IEEE Transactions on Image Processing, vol. 17, no. 7, pp. 1178–1188, 2008.

[21] S. E. Choi, Y. J. Lee, S. J. Lee, K. R. Park and J. Kim, "Age Estimation Using a Hierarchical Classifier Based on Global and Local Facial Features," Pattern Recognit., vol. 44, no. 6, pp. 1262–1281, 2011.

[22] E. Eidinger, R. Enbar and T. Hassner, "Age and Gender Estimation of Unfiltered Faces," IEEE Trans. Inf. FORENSICS Secur., vol. 9, no. 12, pp. 2170–2179, 2014.

[23] Z. Hu, Y. Wen, J. Wang and M. Wang, "Facial Age Estimation with Age Difference," IEEE Trans. IMAGE Process., vol. 7149, no. c, pp. 1–11, 2016.

[24] K. E. Zhang, C. E. Gao, L. Guo, M. Sun and S. Member, "Age Group and Gender Estimation in the Wild with Deep RoR Architecture," Proc. of Chinese Conference on Computer Vision (CCCV), vol. 5, no. Cccv, 2017.

[25] K. Li, J. Xing, W. Hu and S. J. Maybank, "D2C : Deep Cumulatively and Comparatively Learning for Human Age Estimation," Pattern Recognit., vol. 66, no. July 2016, pp. 95–105, 2017.

[26] O. Russakovsky et al., "ImageNet Large Scale Visual Recognition Challenge," Int. J. Comput. Vis. (IJCV), vol. 115, no. 3, pp. 211–252, 2015.

[27] R. Rothe, R. Timofte and L. Van Gool, "Deep Expectation of Real and Apparent Age from a Single Image without Facial Landmarks," Int. J. Comput. Vis., vol. 126, no. 2–4, pp. 144–157, 2016.

[28] H. Liu, J. Lu, J. Fenga and J. Zhou, "Group-aware Deep Feature Learning for Facial Age Estimation," Pattern Recognit., vol. 66, pp. 82–94, 2016.

[29] G. Antipov, M. Baccouche, S. Berrani and J. Dugelay, "Effective Training of Convolutional Neural Networks for Face-based Gender and Age Prediction," Pattern Recognit., vol. 72, pp. 15–26, 2017.

[30] M. Yang, S. Zhu, F. Lv and K. Yu, "Correspondence Driven Adaptation for Human Profile Recognition," Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 505–512, 2011.

[31] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," Proc. of the International Conference on Learning Representations (ICLR), vol. abs/1409.1, 2015.

[32] Z. Hu, Y. Wen, J. Wang and M. Wang, "Facial Age Estimation with Age Difference," IEEE Trans. Image Process., vol. 7149, no. c, pp. 1–11, 2016.

[33] P. Rodríguez, G. Cucurull, J. M. Gonfaus, F. X. Roca and J. Gonzàlez, "Age and Gender Recognition in the Wild with Deep Attention," Pattern Recognit., vol. 72, pp. 563–571, Dec. 2017. 

[34] A. Lanitis, C. Draganova and C. Christodoulou, "Comparing Different Classifiers for Automatic Age Estimation," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 34, no. 1, pp. 621–628, Feb. 2004.

[35] K. Ueki, T. Hayashida and T. Kobayashi, "Subspace-based Age-group Classification Using Facial Images Under Various Lighting Conditions," Proc. of the 7th IEEE International Conference on Automatic Face and Gesture Recognition (FGR06), pp. 1-6, 2006.

[36] A. Lanitis, C. J. Taylor and T. F. Cootes, "Toward Automatic Simulation of Aging Effects on Face Images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp. 442–455, 2002.

[38] Y. Fu, Y. Xu and T. S. Huang, "Estimating Human Age by Manifold Analysis of Face Pictures and Regression on Aging Features," Proc. of IEEE International Conference on Multimedia and Expo, pp. 1383–1386, 2007.

[39] K. Chang and C. Chen, "A Learning Framework for Age Rank Estimation based on Face Images with Scattering Transform," IEEE Trans. Image Process., vol. 7149, no. c, pp. 1–14, 2015.

[40] S. Chen, C. Zhang and M. Dong, "Deep Age Estimation: From Classification to Ranking," IEEE Trans. Multimed., vol. 20, no. 8, 2018.

[41] K. Li, J. Xing, C. Su, W. Hu, Y. Zhang and S. Maybank, "Deep Cost-sensitive and Order-preserving Feature Learning for Cross-population Age Estimation," Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 399–408, 2018.

[42] C. Shang and H. Ai, "Cluster Convolutional Neural Networks for Facial Age Estimation," Proceedings of International Conference on Image Processing (ICIP), pp. 1817–1821, 2017

[43] K. Zhang, N. Liu, X. Yuan, X. Guo, C. Gao and Z. Zhao, "Fine-grained Age Estimation in the Wild with Attention LSTM Networks," CoRR, vol. abs/1805.1, pp. 1–12, 2018.

[44] H. Pan, H. Han, S. Shan and X. Chen, "Mean-variance Loss for Deep Age Estimation From a Face," Presented at the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 5285–5294, 2018.

[45] W. Li, J. Lu, J. Feng, C. Xu, J. Zhou and Q. Tian," BridgeNet: A Continuity-aware Probabilistic Network for Age Estimation," ArXiv190403358 Cs, Apr. 2019.

[46] M. Lin, Q. Chen and S. Yan, "Network In Network," ArXiv13124400 Cs, Dec. 2013.

[47] S. Ioffe and C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," ArXiv150203167 Cs, Feb. 2015.

[48] M. Duan, K. Li and K. Li, "An Ensemble CNN2ELM for Age Estimation," IEEE Trans. Inf. Forensics Secur., vol. 13, no. 3, pp. 758–772, 2018.

[49] C. Shorten and T. M. Khoshgoftaar, "A Survey on Image Data Augmentation for Deep Learning," J. Big Data, vol. 6, no. 1, p. 60, Dec. 2019.

[50] C. M. Bishop, Pattern Recognition and Machine Learning, New York, Springer, 2006.

[51] A. Lanitis and T. Cootes, "Fg-net Aging Data Base," Cyprus Coll., 2002.

[52] Zhang Zhifei, Song, Yang and H. Qi, "Age Progression/Regression by Conditional Adversarial Autoencoder," Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[53] G. Levi and T. Hassner, "Age and Gender Classification using Convolutional Neural Networks," CVPR, vol. 24, no. 3, pp. 2622–2629, 2015.

[54] Z. Niu, M. Zhou, L. Wang, X. Gao and G. Hua, "Ordinal Regression with Multiple Output CNN for Age Estimation," Proc. of IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 4920–4928, 2016.

[55] W. Cao, V. Mirjalili and S. Raschka, "Rank-consistent Ordinal Regression for Neural Networks," ArXiv190107884 Cs Stat, Jan. 2019.

[56] P. Rodríguez, J. M. Gonfaus, G. Cucurull, F. X. Roca and J. Gonzàlez, "Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery," ArXiv180707320 Cs, Jul. 2018.