Age Classification Based on Feature Fusion

Abstract:

Age classification based on facial images is attracting wide attention with its broad application to human-computer interaction (HCI). Since human senescence is a tremendously complex process, age classification is still a highly challenging issue. In our study, Local Directional Pattern (LDP) and Gabor wavelet transform were used to extract global and local facial features, respectively, that were fused based on information fusion theory. The Principal Component Analysis (PCA) method was used for dimensionality reduction of the fused features, to obtain a lower-dimensional age characteristic vector. A Support Vector Machine (SVM) multi-class classifier with Error Correcting Output Codes (ECOC) was proposed in the paper. This was aimed at multi-class classification problems, such as age classification. Experiments on a public FG-NET age database proved the efficiency of our method.

• Periodical: Applied Mechanics and Materials (Volumes 519-520)
• Pages: 644-650
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.519-520.644
• Online since: February 2014
• Authors: Mian Shui Yu*, Yu Xie, Xiao Meng Xie
• Keywords: Age Classification, ECOC, LDP, Support Vector Machine (SVM)

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