TY - JOUR TT - Investigating the Effects of Facial Regions to Age Estimation AU - Günay, Asuman AU - Nabiyev, Vasif PY - 2016 DA - December DO - 10.18100/ijamec.265362 JF - International Journal of Applied Mathematics Electronics and Computers PB - PLUSBASE AKADEMİ ORGANİZASYON VE DANIŞMANLIK WT - DergiPark SN - 2147-8228 SP - 72 EP - 75 IS - Special Issue-1 KW - Age estimation KW - Local Phase Quantization KW - Facial Regions N2 - Aging process causes evident alterations on human facial appearance.Real world age progression on human face is personalized and related with manyfactors such as, genetics, living style, eating habits, facial expressions,climate etc. The wide degree of variations on facial appearance of differentindividuals affects the age estimation performance. In accordance with thesefacts discovering the aging information contained in facial regions is animportant issue in automatic age estimation. Thus the facial regionsemphasizing 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. Forthis purpose, an age estimation method is designed to produce an estimate ofthe age of a subject by using the texture features extracted from facialregions. In this method the facial images are warped into the mean shape thusvariations of head pose and scale are eliminated and the texture information offacial images are aligned. Then the holistic and spatial texture features areextracted from facial regions using Local Phase Quantization (LPQ) texturedescriptor, robust to blur, illumination and expression variations. After thelow dimensional representation of these features, a linear aging function islearned using multiple linear regression. In the experiments FGNET and PALdatabases are used to evaluate the age estimation accuracies of facial regionsi.e. eye, nose, mouth and chin, cheek and sides of mouth, separately. Theresults have shown that the eye region carries the most significant informationfor age estimation. Also the mouth and chin, cheek regions are effective in theprediction of age. The results also have shown that, using the spatial texturefeatures enhances the discriminative power of the texture descriptor and thusincreases the estimation accuracy. CR - [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. CR - [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. CR - [3] Dehshibi M. M. and Bastanfard A. 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UR - https://doi.org/10.18100/ijamec.265362 L1 - https://dergipark.org.tr/en/download/article-file/231757 ER -