Abstract
There is a growing research interest in facial aging estimation where the whole facial area was used for age estimation. However, in this paper, we propose a different approach for age estimation whereby we perform feature extraction at the specific part which is called the Region of Interest (ROI) on the upper facial area by employing a specific orientation and scales of Gabor filter. The proposed multi-Support Vector Machine (SVM) was used as the classification, and tested on two databases which are the captured face images of Malaysian citizen, and the FG-NET database. For the scheme, the Leave One Picture Out (LOPO) is used for training and testing according to age groups. The overall results of the proposed method show that the upper ROI performances are better for both Malaysian citizen and FG-NET database than the full facial ROI. Moreover, the Mean Absolute Error (MAE) for the FG-NET decreases when using the upper ROI approach.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In Malaysian database, a total of 170 image samples was used; for the FG-NET database, we only use sample image age 20 and above, giving a total of 172 image used.
References
Lanitis A (2002) On the significance of different facial parts for automatic age estimation. In: 14th international conference on digital signal processing, vol 2, pp 1027–1030
El Dib MY, Onsi HM (2011) Human age estimation framework using different facial parts. Egypt Inform J 12(1):53–59
Ramanathan N, Chellapa R, Biswas S (2009) Computational methods for modeling facial aging: a survey. J Vis Lang Comput 20(3):131–144
Ramanathan N, Chellapa R, Biswas S (2009) Age progression in human faces: a survey. J Vis Lang Comput 15:3349–3361
Zimbler MS, Kokosa M, Thomas JR (2001) Anatomy and pathophysiology of facial aging. Facial Plast Surg Clin N Am 9:179–187
Chung JH (2003) Photoaging in Asians. Photodermatol Photoimmunol Photomed 19:109–121. doi:10.1034/j.1600-0781.2003.00027.x
Shirakabe Y, Suzuki Y, Lam SM (2003) A new paradigm for the aging Asian face. Aesthetic Plast Surg 27:397–402
Wu Y, Beylot P, Thalmann NM (1999) Skin aging estimation by facial simulation. In: Proceeding computer animation, pp 210
Kwon Y, Lobo N (1994) Age classification from facial images. In: Proceeding computer vision and pattern recognition, pp 762–767
Wen-Bing H, Cheng-Ping L, Chun-Wen C (2001) Classification of age groups based on facial features. Tamkang J Sci Eng 4(3):183–192
Txia J, Huang C (2009) Age estimation using AAM and local facial features. In: Fifth international conference on intelligent information hiding and multimedia signal processing, pp 885–888
Ramesha K et al (2010) Feature extraction based face recognition, gender and age classification. Int J Comput Sci Eng 02(1):14–23
Fu Y, Xu Y, Huang TS (2007) Estimating human age by manifold analysis of face pictures and regression on aging features. In: IEEE international conference on multimedia and expo, pp 1383–1386
Zhuang X, Zhou X, Hasegawa-Johnson M, Huang T (2008) Face age estimation using patch-based hidden markov model supervectors. In: 19th international conference on pattern recognition ICPR 2008, pp1–4
Mokadem A, Charbit M, Chollet G, Bailly K (2010) Age regression based on local image features. In: 2010 fourth pacific-rim symposium on image and video technology (PSIVT), pp 88–93
Guo J, Liou Y, Nguyen H (2011) Human face age estimation with adaptive hybrid features. In: 2011 international conference on system science and engineering (ICSSE), pp 55–58
Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybern B Cybern 34(1):621–628
Luu K, Ricanek K, Bui TD, Suen CY (2009) Age estimation using active appearance models and support vector machine regression. In: IEEE 3rd international conference on biometrics: theory, applications, and systems, pp 1–5
Choi SE, Lee YJ, Lee SJ, Park KR, Kim J (2011) Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recogn 44(6):1262–1281
Pirozmand P, Amiri MF, Kashanchi F, Layne NY (2011) Age estimation, a gabor PCA-LDA approach. J Math Comput Sci (JMCS) 2(2):233–240
Guo G, Mu G, Fu Y, Huang TS (2009) Human age estimation using bio-inspired features. In: IEEE conference on computer vision and pattern recognition CVPR 2009, pp 112–119
Takimoto H, Mitsukura Y, Fukumi M, Akamatsu N (2008) Robust gender and age estimation under varying facial pose. Electron Commun Japan 91(7):32–40
Manjunathi BS, Ma WY (1996) Texture features for browsing, retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842
FGNET (2012) The fg-net aging database (2002), http://www.fgnet.rsunit.com/. Accessed 20 April 2012
Aldrian P (2009) Matlab central : fast eye tracking. http://www.mathworks.com/matlabcentral/fileexchange/25056-fast-eyetracking. Accessed 2 Mei 2012
Mishra A (2011) Matlab central : multi class support vector machine. http://www.mathworks.com/matlabcentral/fileexchange/33170-multi-class-support-vector-machine. Accessed 10 June 2012
Pham TV (2012) Perth facial plastic and cosmetic surgery. http://www.perthcosmeticsurgery.com.au/wrinkle_management_procedure.do. Accessed 25 June 2012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Dahlan, H.A., Mashohor, S., Rahman, S.M.S.A.A., Adnan, W.A.W. (2013). Age Estimation Using Specific Gabor Filter on Upper Facial Area. In: S, M., Kumar, S. (eds) Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 221. Springer, India. https://doi.org/10.1007/978-81-322-0997-3_56
Download citation
DOI: https://doi.org/10.1007/978-81-322-0997-3_56
Published:
Publisher Name: Springer, India
Print ISBN: 978-81-322-0996-6
Online ISBN: 978-81-322-0997-3
eBook Packages: EngineeringEngineering (R0)