Practical age estimation using deep label distribution learning

Age estimation plays an important role in humancomputer interaction system. The lack of large number of facial images with definite age label makes age estimation algorithms inefficient. Deep label distribution learning (DLDL) which employs convolutional neural networks (CNN) and label distribution learning to learn ambiguity from ground-truth age and adjacent ages, has been proven to outperform current state-of-the-art framework. However, DLDL assumes a rough label distribution which covers all ages for any given age label. In this paper, a more practical label distribution paradigm is proposed: we limit age label distribution that only covers a reasonable number of neighboring ages. In addition, we explore different label distributions to improve the performance of the proposed learning model. We employ CNN and the improved label distribution learning to estimate age. Experimental results show that compared to the DLDL, our method is more effective for facial age recognition.

Automated summary

This study proposes a more practical approach for facial age recognition, which limits the age label distribution to only cover a reasonable number of neighboring ages, and explores different label distributions to improve model performance. The experimental results show that the proposed method is more effective for facial age recognition compared to the current state-of-the-art framework DLDL.