NEWS

A SURVEY ON AGE-INVARIANT FACE RECOGNITION METHODS


(Received: 2019-04-09, Revised: 2019-06-05 , Accepted: 2019-06-23)
Face recognition is used in many security and surveillance applications. Some issues, such as aging, partial occlusion, variation in pose and illumination and facial expression directly affect the performance of face recognition approaches. Usually, in many applications, such as checking the passport and visa, images in the database are not updated continuously. In these cases, aging leads to change the important features of the face image. Hence, face recognition across aging can be considered as a common issue in many security and surveillance systems. In this paper, some existing face recognition approaches, in terms of robustness to aging, have been reviewed briefly. Also, the experimental results from these methods have been compared using the common databases in age-invariant face recognition applications. The comparison results indicate that the approaches which consider both component-based representation of facial images and identity factors outperform the other existing methods.

[1] M. Chihaoui, A. Elkefi, W. Bellil and C. Ben Amar, "A Survey of 2D Face Recognition Techniques," Computers, vol. 5, no. 4, p. 21, 2016.

[2] M. Sharif, F. Naz, M. Yasmin, M. A. Shahid and A. Rehman," Face Recognition: A Survey," Journal of Engineering Science & Technology Review, vol. 10, no. 2, 2017.

[3] M. M. Sawant and K. M. Bhurchandi, "Age Invariant Face Recognition: A Survey on Facial Aging Databases, Techniques and Effect of Aging," Artificial Intelligence Review, pp. 1-28, 2018.

[4] A. K. Agrawal and Y. N. Singh, "Evaluation of Face Recognition Methods in Unconstrained Environments," Procedia Computer Science, vol. 48, pp. 644-651, 2015.

[5] S. Sahni and S. Saxena, "Survey: Techniques for Aging Problems in Face Recognition," MIT International Journal of Computer Science and Information Technology, vol. 4, no. 2, pp. 82-88, 2014.

[6] L. Best-Rowden and A. K. Jain, "Longitudinal Study of Automatic Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 1, pp. 148-162, 2018.

[7] H. Liao, Y. Yan, W. Dai and P. Fan, "Age Estimation of Face Images Based on CNN and Divide-and- Rule Strategy," Mathematical Problems in Engineering, 2018.

[8] H. Liu, J. Lu, J. Feng and J. Zhou, "Group-aware Deep Feature Learning for Facial Age Estimation," Pattern Recognition, vol. 66, pp. 82-94, 2017.

[9] 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, vol. 24, no. 3, pp. 785-798, 2015.

[10] J. Lu, V. E. Liong and J. Zhou,"Cost-sensitive Local Binary Feature Learning for Facial Age Estimation," IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 5356-5368, 2015.

[11] X. Yang, B. B. Gao, C. Xing, Z. W. Huo, X. S. Wei, Y. Zhou, J. Wu and X. Geng, "Deep Label Distribution Learning for Apparent Age Estimation," Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 102-108, 2015.

[12] I. Huerta, C. Fernández, C. Segura, J. Hernando and A. Prati, "A Deep Analysis on Age Estimation," Pattern Recognition Letters, vol. 68, pp. 239-249, 2015.

[13] J. Ylioinas, A. Hadid, X. Hong and M. Pietikäinen, "Age Estimation Using Local Binary Pattern Kernel Density Estimate," Int. Conf. on Image Analysis and Processing, pp. 141-150, 2013.

[14] A. Montillo and H. Ling, "Age Regression from Faces Using Random Forests," International Conference on Image Processing (ICIP), pp. 2465-2468, 2009.

[15] G. Guo, G. Mu, Y. Fu and T. S. Huang, "Human Age Estimation Using Bio-inspired Features," Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 112-119, 2009.

[16] Y. Fu and T. S. Huang, "Human Age Eestimation with Regression on Discriminative Aging Manifold," IEEE Transactions on Multimedia, vol. 10, no. 4, pp. 578-584, 2008.

[17] S. K. Zhou, B. Georgescu, X. S. Zhou and D. Comaniciu, "Image-based Regression Using Boosting Method," Proc. of the IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 541-548, 2005.

[18] 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.

[19] 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.

[20] Y. H. Kwon and N. da Vitoria Lobo, "Age Classification from Facial Images," Computer Vision and Image Understanding, vol. 74, no. 1, pp. 1-21, 1999.

[21] J. Suo, X. Chen, S. Shan and W. Gao, "Learning Long Term Face Aging Patterns from Partially Dense Aging Databases," Proc. of International Conference on Computer Vision, pp. 622-629, 2009.

[22] A. Lanitis, C. J. Taylor and T. F. Cootes, "Toward Automatic Simulation of Aging Effects on Face Images," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 4,pp. 442-455, 2002.

[23] J. Suo, S. C. Zhu, S. Shan and X. Chen, "A Compositional and Dynamic Model for Face Aging," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 385-401, 2010. 

[24] N. Tsumura, N. Ojima, K. Sato, M. Shiraishi, H. Shimizu, H. Nabeshima, S. Akazaki, K. Hori and Y. Miyake, "Image-based Skin Color and Texture Analysis/Synthesis by Extracting Hemoglobin and Melanin Information in the Skin," ACM Trans. on Graphics (TOG), vol. 22, no.3, pp. 770-779, 2003.

[25] J. X. Du, C. M. Zhai and Y. Q. Ye, "Face Aging Simulation Based on NMF Algorithm with Sparseness Constraints," International Conference on Intelligent Computing, pp. 516-522. Springer, Berlin, Heidelberg, 2011.

[26] J. Wang, Y. Shang, G. Su and X. Lin, "Age Simulation for Face Recognition," Proc. of International Conference on Pattern Recognition (ICPR), vol. 3, pp. 913-916, 2006.

[27] N. Ramanathan and R. Chellappa, "Modeling Shape and Textural Variations in Aging Faces," Proc. of IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1-8, 2008.

[28] J. X. Du, C. M. Zhai and Y. Q. Ye, "Face Aging Simulation and Recognition Based on NMF Algorithm with Sparseness Constraints," Neurocomputing, vol. 116, pp. 250-259, 2013.

[29] 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.

[31] P. O. Hoyer, "Non-negative Matrix Factorization with Sparseness Constraints," Journal of Machine Learning Research, pp. 1457-1469, 2004.

[32] G. Mahalingam and C. Kambhamettu, "Age Invariant Face Recognition Using Graph Matching," Proc. of the IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS), pp. 1- 7, 2010.

[33] P. S. Penev and J. J Atick, "Local Feature Analysis: A General Statistical Theory for Object Representation," Networks: Computation in Neural Systems, vol. 7, no. 3, pp. 477-500, 1996.

[34] R. Duda, P. Hart and D. Stork, Pattern Classification, John Wiley & Sons, New York, 2nd Edition, 2001.

[35] N. Ramanathan and R. Chellappa, "Face Verification across Age Progression," IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3349-3361, 2006.

[36] 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.

[38] H. Ling, S. Soatto, N. Ramanathan and D. W. Jacobs, "A Study of Face Recognition As People Age," Proc. of the International Conference on Computer Vision (ICCV), pp. 1-8, 2007.

[39] 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.

[40] D. Sungatullina, J. Lu, G. Wang and P. Moulin, "Multiview Discriminative Learning for Age-invariant Face Recognition," Proc. of the International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1-6, 2013.

[41] C. Otto, H. Han and A. Jain, "How Does Aging Affect Facial Components?," European Conference on Computer Vision, pp. 189-198, Springer, Berlin, Heidelberg, 2012.

[42] T. F. Cootes, C. J. Taylor, D. H. Cooper and J. Graham, "Active Shape Models-Their Training and Application," Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38-59, 1995.

[43] F. Juefei-Xu, K. Luu, M. Savvides, T. D. Bui and C. Y. Suen, "Investigating Age Invariant Face Recognition Based on Periocular Biometrics," Proc. of the Int. Joint Conf. on Biometrics, pp. 1-7, 2011.

[44] D. Gong, Z. Li, D. Lin, J. Liu and X. Tang, "Hidden Factor Analysis for Age Invariant Face Recognition," Proc. of IEEE Int. Conf. on Computer Vision (ICCV), pp. 2872-2879, 2013.

[45] D. Gong, Z. Li, D. Tao, J. Liu and X. Li, "A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 5289-5297, 2015.

[46] H. Li, H. Zou and H. Hu, "Modified Hidden Factor Analysis for Cross-age Face Recognition," IEEE Signal Processing Letters, vol. 24, no. 4, pp. 465-469, 2017.

[47] C. Xu, Q. Liu and M. Ye, "Age Invariant Face Recognition and Retrieval by Coupled Auto-encoder Networks," Neurocomputing, vol. 222, pp. 62-71, 2017.

[48] Y. Wen, Z. Li and Y. Qiao, "Latent Factor Guided Convolutional Neural Networks for Age-invariant Face Recognition," Proc. of IEEE Conf. on Comp. Vision and Pattern Recog., pp. 4893-4901, 2016. 

[49] M. Sajid, T. Shafique, S. Manzoor, F. Iqbal, H. Talal, U. Samad Qureshi and I. Riaz, "Demographic- Assisted Age-invariant Face Recognition and Retrieval," Symmetry, vol. 10, no. 5, pp. 1-17, 2018.

[50] O. M. Parkhi, A. Vedaldi and A. Zisserman, "Deep Face Recognition," Proceedings of the British Machine Vision Conference (BMVC), vol. 1, no. 3, p. 6, Swansea, UK, 2015.

[51] T. Zheng, W. Deng and J. Hu, "Age Estimation Guided Convolutional Neural Network for Age- invariant Face Recognition," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-9, 2017.

[52] Y. Li, G. Wang, L. Nie, Q. Wang and W. Tan, "Distance Metric Optimization Driven Convolutional Nneural Network for Age Invariant Face Recognition," Pattern Recognition, vol. 75, pp. 51-62, 2018.

[53] T. Cootes, "FGNET Face and Gesture Recognition Database," Face and Gesture Recognition Working Group,[Online], Available at: http://www-prima.inrialpes.fr/FGnet/.

[54] K. Ricanek and T. Tesafaye, "Morph: A Longitudinal Image Database of Normal Adult Age- Progression," Proc. of the Int. Conf. on Aut. Face and Gesture Recognition (FGR), pp. 341-345, 2006.

[55] B. C. Chen, C. S. Chen and W. H. Hsu, "Cross-age Reference Coding for Age-invariant Face Recognition and Retrieval," Proc. of the European Conference on Computer Vision, pp. 768-783, Springer, Cham, 2014.

[56] B. C. Chen, C. S. Chen and W. H. Hsu, "Face Recognition and Retrieval Using Cross-age Reference Coding with Cross-age Celebrity Dataset," IEEE Transactions on Multimedia, vol. 17, no. 6, pp. 804- 815, 2015.

[57] S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia and S. Zafeiriou, "AgeDB: The First Manually Collected, in-the-Wild Age Database," Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 51-59, 2017.

[58] K. Delac, M. Grgic and S. Grgic,"Statistics in Face Recognition: Analyzing Probability Distributions of PCA, ICA and LDA Performance Results," Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 289-294, 2005.

[59] M. Wang and W. Deng, "Deep Face Recognition: A Survey," arXiv preprint arXiv:1804.06655, 2018.