Facial age estimation based on label-sensitive learning and age-specific local regression

In this paper, a new age estimation framework considering the intrinsic properties of human ages is proposed, which improves the dimensionality reduction techniques to learn the connections between facial features and aging labels. To enhance the performance of dimensionality reduction, a distance metric adjustment step is introduced in advance to achieve a suitable metric in the feature space. In addition, to further exploit the ordinal relationship of human ages, the "label-sensitive" concept is proposed, which regards the label similarity during the learning phase of distance metric and dimensionality reduction. Finally, an age-specific local regression algorithm is proposed to capture the complicated aging process for age determination. From the simulation results, the proposed framework achieves the lowest mean absolute error against the existing methods.