Geometric feature based age classification using facial images

This paper presents the use of geometric feature based models for age group determination of facial color images. This process consists of two main stages: geometric feature extraction, analysis and age group classification. The feature extraction was performed with the correct understanding of the effect of age on facial anthropometry. The age differentiation capability of the features is evaluated using three different classifiers, namely, neural network classifier, support vector classifier, normal densities-based linear classifier. The facial face images are categorized to five major age groups. To show the effectiveness and accuracy of the proposed feature extraction, experiments are conducted on two publically available databases namely FGNET and IFDB. The results show that the success rate of classification is around 90%. (5 pages)

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