Investigating the Effects of Facial Regions to Age Estimation

Yıl 2016, Special Issue (2016), 72 - 75, 01.12.2016

Asuman Günay , Vasif Nabiyev

https://doi.org/10.18100/ijamec.265362

Öz

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.

Anahtar Kelimeler

Age estimation, Local Phase Quantization, Facial Regions

Kaynakça

• [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.