Özet:

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.

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