Investigating the Effects of Facial Regions to Age Estimation

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

Asuman Günay , Vasif Nabiyev

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

Abstract

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.

Keywords

Age estimation, Local Phase Quantization, Facial Regions

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