Subspace-based age-group classification using facial images under various lighting conditions

This paper presents a framework of age-group classification using facial images under various lighting conditions. Our method is based on the appearance-based approach that projects images from the original image space into a face-subspace. We propose a two-phased approach (2DLDA+LDA), which is based on 2DPCA and LDA. Our experimental results show that the new 2DLDA+LDA-based approach improves classification accuracy more than the conventional PCA-based and LDA-based approach. Moreover, the effectiveness of eliminating dimensions that do not contain important discriminative information is confirmed. The accuracy rates are 46.3%, 67.8% and 78.1% for age-groups that are in the 5-year, 10-year and 15-year range respectively