Mining Wrinkle-Patterns with Local Edge-Prototypic Pattern (LEPP) Descriptor for the Recognition of Human Age-groups

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Md Tauhid Bin Iqbal 1,* Oksam Chae 1

1. Dept. of Computer Science and Engineering, Kyung Hee University (Global Campus), Giheung-gu, Yongin-si, Gyeonggi-do, 17104, South Korea

* Corresponding author.


Received: 11 Apr. 2018 / Revised: 28 Apr. 2018 / Accepted: 15 May 2018 / Published: 8 Jul. 2018

Index Terms

Mining winkle, Age-group Classification, (LEPP), Aging-cue, Wrinkle-patterns, Noise, (ADIENCE), (GALLAGHER), (FACES)


Human age recognition from face image relies highly on a reasonable aging description. Considering the disparate and complex face-aging variation of each person, aging description needs to be defined carefully with detailed local information. However, aging description relies highly on the appropriate definition of different aging-affiliated textures. Wrinkles are considered as the most discernible textures in this regard owing to their significant visual appearance in human aging. Most of the existing image-descriptors, however, fail short to preserve diverse variations of wrinkles, such as a) characterizing stronger and smoother wrinkles, appropriately, b) distinguishing wrinkles from non-wrinkle patterns, and c) characterizing the proper texture-structures of the pixels belonging to the same wrinkle. In this paper, we address these issues by presenting a new local descriptor, Local Edge-Prototypic Pattern (LEPP) with the notion that LEPP preserves different variations of wrinkle-patterns appropriately in representing the aging description. In the coding, LEPP sets prototypic restrictions for each neighboring pixel using their relation with center pixel when they belong to an inlying-edge, and utilize such restrictions, afterwards, to prioritize specific neighbors showing significant edge-signature. This strategy appropriately encodes the inlying edge structure of aging-affiliated textures and simultaneously, avoids featureless texture. We visualize the stability of LEPP in terms of its robustness under noise. Our experiments show that LEPP preserves discernible aging variations yielding better accuracies than the state-of-the-art methods in popular age-group datasets.

Cite This Paper

Md Tauhid Bin Iqbal, Oksam Chae, " Mining Wrinkle-Patterns with Local Edge-Prototypic Pattern (LEPP) Descriptor for the Recognition of Human Age-groups ", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.7, pp. 1-10, 2018. DOI: 10.5815/ijigsp.2018.07.01


[1]Y. Fu, G. Guo, and T. S. Huang. Age synthesis and estimation via faces: A survey. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(11):1955–1976, 2010.

[2]N. Ramanathan, R. Chellappa, and S. Biswas. Computational methods for modeling facial aging: A survey. Journal of Visual Languages & Computing, 20(3):131–144, 2009.

[3]Y. Fu and T. S. Huang. Human age estimation with regression on discriminative aging manifold. Multimedia, IEEE Transactions on, 10(4):578–584, 2008. 1

[4]N. Ramanathan and R. Chellappa. Face verification across age progression. IEEE Transactions on Image Processing, 15(11):3349–3361, 2006.

[5]A.M. Albert, K. Ricanek, Jr., and E. Patterson, “A review of the literature on the aging adult skull and face: Implications for forensic science research and applications,” Forensic Sci. Int., vol. 172, no. 1, pp. 1–9, 2007.

[6]M. Albert, A. Sethuram, and K. Ricanek, Implications of Adult Facial Aging on Biometrics. Rijeka, Croatia: InTech, 2011.

[7]E. Patterson, A. Sethuram, M. Albert, K. Ricanek, and M. King, “Aspects of age variation in facial morphology affecting biometrics,” in Proc. 1st IEEE Int. Conf. Biometrics, Theory, Appl., Syst. (BTAS), Sep. 2007, pp. 1–6.

[8]A. Lanitis, “A survey of the effects of aging on biometric identity verification,” Int. J. Biometrics, vol. 2, no. 1, pp. 34–52, Dec. 2010.

[9]Y. Bando, T. Kuratate, and T. Nishita. A simple method for modeling wrinkles on human skin. In Computer Graphics and Applications, 2002. Proceedings. 10th Pacific Conference on, pages 166–175. IEEE, 2002.

[10]B. Tiddeman, M. Burt, and D. Perrett. Prototyping and transforming facial textures for perception research. IEEE computer graphics and applications, 21(5):42–50, 2001.

[11]Y. Wu, P. Kalra, L. Moccozet, and N. Magnenat-Thalmann. Simulating wrinkles and skin aging. The visual computer, 15(4):183–198, 1999

[12]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 Recognition, 44(6):1262–1281, 2011. 

[13]M. M. Dehshibi and A. Bastanfard. A new algorithm for age recognition from facial images. Signal Processing, 90(8):2431–2444, 2010

[14]M. A. Hajizadeh and H. Ebrahimnezhad. Classification of age groups from facial image using histograms of oriented gradients. In Machine Vision and Image Processing (MVIP), 2011 7th Iranian, pages 1–5. IEEE, 2011

[15]J.-i. Hayashi, M. Yasumoto, H. Ito, and H. Koshimizu. Age and gender estimation based on wrinkle texture and color of facial images. In Pattern Recognition, 2002. Proceedings. 16th International Conference on, volume 1, pages 405–408. IEEE, 2002

[16]W.-B. Horng, C.-P. Lee, and C.-W. Chen. Classification of age groups based on facial features. Tamkang Journal of Science and Engineering, 4(3):183–192, 2001 

[17]A. Lanitis, C. J. Taylor, and T. F. Cootes. Modeling the process of ageing in face images. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 1, pages 131–136. IEEE, 1999

[18]K. Luu, K. Ricanek Jr, T. D. Bui, and C. Y. Suen. Age estimation using active appearance models and support vector machine regression. In Biometrics: Theory, Applications, and Systems, 2009. BTAS’09. IEEE 3rd International conference on, pages 1–5. IEEE, 2009

[19]X. Geng, Z.-H. Zhou, and K. Smith-Miles. Automatic age estimation based on facial aging patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 29(12):2234–2240, 2007.

[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. Image Processing, IEEE Transactions on, 17(7):1178–1188, 2008

[21]J. Lu and Y.-P. Tan. Ordinary preserving manifold analysis for human age and head pose estimation. Human-Machine Systems, IEEE Transactions on, 43(2):249–258, 2013.

[22]J.-C. Chen, A. Kumar, R. Ranjan, V. M. Patel, A. Alavi, and R. Chellappa. A cascaded convolutional neural network for age estimation of unconstrained faces. In Biometrics Theory Applications and Systems (BTAS), 2016 IEEE 8th International Conference on, pages 1–8. IEEE, 2016.

[23]G. Levi and T. Hassner. Age and gender classification using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 34–42, 2015.

[24]G¨unay and V. V. Nabiyev. Automatic age classification with lbp. In Computer and Information Sciences, 2008. ISCIS’ 08. 23rd International Symposium on, pages 1–4. IEEE, 2008.

[25]J. Ylioinas, A. Hadid, and M. Pietikainen. Age classification in unconstrained conditions using lbp variants. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 1257–1260. IEEE, 2012

[26]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

[27]M. T. B. Iqbal, B. Ryu, G. Song, and O. Chae. Positional ternary pattern (ptp): An edge based image descriptor for human age recognition. In 2016 IEEE International Conference on Consumer Electronics (ICCE), pages 289–292, IEEE, 2016.

[28]M. T. B. Iqbal, M. Shoyaib, B. Ryu, M. Abdullah-Al-Wadud, and O. Chae. "Directional age-primitive pattern (DAPP) for human age group recognition and age estimation." IEEE Transactions on Information Forensics and Security12.11 (2017): 2505-2517.

[29]T. Jabid, M. H. Kabir, and O. Chae. Robust facial expression recognition based on local directional pattern. ETRI journal, 32(5):784–794, 2010

[30]A. Ramirez Rivera, J. Rojas Castillo, and O. Chae. Local directional number pattern for face analysis: Face and expression recognition. Image Processing, IEEE Transactions on, 22(5):1740–1752, 2013.

[31]A. C. Bovik, Handbook of image and video processing. Academic press, 2010.

[32]L. Shapiro, Computer vision and image processing. Academic Press, 1992.

[33]A. C. Gallagher and T. Chen. Understanding images of groups of people. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 256–263. IEEE, 2009.

[34]N. C. Ebner, M. Riediger, and U. Lindenberger. Faces-A database of facial expressions in young, middle-aged, and older women and men: Development and validation. Behavior research methods, 42(1):351–362, 2010.

[35]C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415–425, Mar. 2002

[36]J. Ylioinas, A. Hadid, Y. Guo, and M. Pietikäinen, “Efficient image appearance description using dense sampling based local binary patterns,” in Computer Vision. Berlin, Germany: Springer, 2013, pp. 375–388.