Investigating the Effects of Facial Regions to Age Estimation

Asuman Günay [1] , Vasif Nabiyev [2]


Aging process causes evident alterations on human facial appearance. Real world age progression on human face is personalized and related with many factors such as, genetics, living style, eating habits, facial expressions, climate etc. The wide degree of variations on facial appearance of different individuals affects the age estimation performance. In accordance with these facts discovering the aging information contained in facial regions is an important issue in automatic age estimation. Thus the facial regions emphasizing the aging information can be used for more accurate age estimation. In this context, age estimation performances of facial regions (eye, nose, mouth and chin, cheeks and sides of mouth) are investigated in this paper. For this purpose, an age estimation method is designed to produce an estimate of the age of a subject by using the texture features extracted from facial regions. In this method the facial images are warped into the mean shape thus variations of head pose and scale are eliminated and the texture information of facial images are aligned. Then the holistic and spatial texture features are extracted from facial regions using Local Phase Quantization (LPQ) texture descriptor, robust to blur, illumination and expression variations. After the low dimensional representation of these features, a linear aging function is learned using multiple linear regression. In the experiments FGNET and PAL databases are used to evaluate the age estimation accuracies of facial regions i.e. eye, nose, mouth and chin, cheek and sides of mouth, separately. The results have shown that the eye region carries the most significant information for age estimation. Also the mouth and chin, cheek regions are effective in the prediction of age. The results also have shown that, using the spatial texture features enhances the discriminative power of the texture descriptor and thus increases the estimation accuracy.
Age estimation, Local Phase Quantization, Facial Regions
  • [1] Kwon Y. H. and Lobo N. V. Age Classification from Facial Images, Computer Vision and Image Understanding, Vol. 74, No. 1, 1999, pp. 1-21.
  • [2] Horng W. B., Lee C. P. and Chen C. W. Classification of Age Groups Based on Facial Features, Tamkang Journal of Science and Engineering, Vol. 4, No. 3, 2001, pp. 183-192.
  • [3] Dehshibi M. M. and Bastanfard A. A new algorithm for age recognition from facial images, Signal Processing, Vol. 90, No. 8, 2010, pp. 2431-2444.
  • [4] Lanitis A., Taylor C. and Cootes T. Toward Automatic Simulation of Aging Effects on Face Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 4, 2002, pp. 442-455.
  • [5] Kohli S., Prakash S. and Gupta P. Hierarchical age estimation with dissimilarity-based classification, Neurocomputing, Vol. 120, 2013, pp. 164-176.
  • [6] Chao W. L., Liu J. Z. and Ding J. J. Facial age estimation based on label-sensitive learning and age oriented regression, Pattern Recognition, Vol. 43, 2013, pp. 628-641.
  • [7] Choi S. E., Le Y. J., Lee S. J., Park K. R. and Kim J. Age estimation using a hierarchical classifier based on global and local facial features, Pattern Recognition, Vol. 44, 2011, pp. 1262-1281.
  • [8] Geng X., Zhou Z. H. and Miles K. S. Automatic Age Estimation Based on Facial Aging Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 12, 2007, pp. 2234-2240.
  • [9] Fu Y. and Huang T. S. Human Age Estimation with Regression on Discriminative Aging Manifold, IEEE Transactions on Multimedia, Vol. 10, No. 4, 2008, pp. 578-584.
  • [10] Guo G., Fu Y., Dyer C. R. and Huang T. S. Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression, IEEE Transactions on Image Processing, Vol. 17, No. 7, 2008, pp. 1178-1188.
  • [11] Chen C., Yang W., Wang Y., Ricanek K. and Luu K. Facial Feature Fusion and Model Selection for Age Estimation, IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG’11), 2011, pp. 200-205.
  • [12] Guo G., Mu G., Fu Y. and Huang T. S. Human Age Estimation Using Bio-Inspired Features, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 112-119.
  • [13] J. Liu, Y. Ma, L. Duan, F. Wang and Y. Liu, “Hybrid constraint SVR for facial age estimation”, Signal Processing, vol. 94, pp. 576-582, 2014.
  • [14] Lanitis A. On the Significance of Different Facial Parts for Automatic Age Estimation, 14th International Conference on Digital Signal Processing, Vol. 2, 2002, pp. 1027-1030.
  • [15] El Dib M. Y. and Onsi H. M. Human age estimation framework using different facial parts, Egyptian Informatics Journal, Vol. 12, No. 1, 2011, pp. 53-59.
  • [16] Ojansivu V. and Heikkila J. Blur Insensitive Texture Classification Using Local Phase Quantization, Image and Signal Processing, Vol. 5099, 2008, pp. 236-243.
  • [17] FG-Net aging database. Available: http://sting.cycollege. ac.cy /~alanitis/fgnetaging. May 2006.
  • [18] Minear M. and Park D. C. A lifespan database of adult stimuli, Behavior Research Methods, Instruments and Computers, Vol.36, No.4, 2004, pp.630-633.
Subjects Engineering
Journal Section Research Article
Authors

Author: Asuman Günay
Institution: KARADENIZ TEKNIK UNIV
Country: Turkey


Author: Vasif Nabiyev
Institution: KARADENIZ TEKNIK UNIV
Country: Turkey


Dates

Publication Date : December 1, 2016

Bibtex @conference paper { ijamec265362, journal = {International Journal of Applied Mathematics Electronics and Computers}, issn = {}, eissn = {2147-8228}, address = {}, publisher = {Selcuk University}, year = {2016}, volume = {}, pages = {72 - 75}, doi = {10.18100/ijamec.265362}, title = {Investigating the Effects of Facial Regions to Age Estimation}, key = {cite}, author = {Günay, Asuman and Nabiyev, Vasif} }
APA Günay, A , Nabiyev, V . (2016). Investigating the Effects of Facial Regions to Age Estimation. International Journal of Applied Mathematics Electronics and Computers , (Special Issue-1) , 72-75 . DOI: 10.18100/ijamec.265362
MLA Günay, A , Nabiyev, V . "Investigating the Effects of Facial Regions to Age Estimation". International Journal of Applied Mathematics Electronics and Computers (2016 ): 72-75 <https://dergipark.org.tr/en/pub/ijamec/issue/25619/265362>
Chicago Günay, A , Nabiyev, V . "Investigating the Effects of Facial Regions to Age Estimation". International Journal of Applied Mathematics Electronics and Computers (2016 ): 72-75
RIS TY - JOUR T1 - Investigating the Effects of Facial Regions to Age Estimation AU - Asuman Günay , Vasif Nabiyev Y1 - 2016 PY - 2016 N1 - doi: 10.18100/ijamec.265362 DO - 10.18100/ijamec.265362 T2 - International Journal of Applied Mathematics Electronics and Computers JF - Journal JO - JOR SP - 72 EP - 75 VL - IS - Special Issue-1 SN - -2147-8228 M3 - doi: 10.18100/ijamec.265362 UR - https://doi.org/10.18100/ijamec.265362 Y2 - 2016 ER -
EndNote %0 International Journal of Applied Mathematics Electronics and Computers Investigating the Effects of Facial Regions to Age Estimation %A Asuman Günay , Vasif Nabiyev %T Investigating the Effects of Facial Regions to Age Estimation %D 2016 %J International Journal of Applied Mathematics Electronics and Computers %P -2147-8228 %V %N Special Issue-1 %R doi: 10.18100/ijamec.265362 %U 10.18100/ijamec.265362
ISNAD Günay, Asuman , Nabiyev, Vasif . "Investigating the Effects of Facial Regions to Age Estimation". International Journal of Applied Mathematics Electronics and Computers / Special Issue-1 (December 2016): 72-75 . https://doi.org/10.18100/ijamec.265362
AMA Günay A , Nabiyev V . Investigating the Effects of Facial Regions to Age Estimation. International Journal of Applied Mathematics Electronics and Computers. 2016; (Special Issue-1): 72-75.
Vancouver Günay A , Nabiyev V . Investigating the Effects of Facial Regions to Age Estimation. International Journal of Applied Mathematics Electronics and Computers. 2016; (Special Issue-1): 75-72.