Deep Age Estimation: From Classification to Ranking

Human age is considered an important biometric trait for human identification or search. Recent research shows that the aging features deeply learned from large-scale data lead to significant performance improvement on facial image-based age estimation. However, age-related ordinal information is totally ignored in these approaches. In this paper, we propose a novel convolutional neural network (CNN)-based framework, ranking-CNN, for age estimation. Ranking-CNN contains a set of basic CNNs, each of which is trained with ordinal age labels. Then, their binary outputs are aggregated for the final age prediction. From a theoretical perspective, we obtain an approximation for the final ranking error, show that it is controlled by the maximum error produced among subranking problems, and thus find a new error bound, which provides helpful guidance for the training and analysis of deep rankers. Based on the new error bound, we theoretically give an explicit formula for the learning of ranking-CNN and demonstrate its convergence using the stochastic approximation method. Moreover, we rigorously prove that ranking-CNN, by considering ordinal relation between ages, is more likely to get smaller estimation errors when compared with multiclass classification approaches. Through extensive experiments, we show that ranking-CNN outperforms other state-of-the-art feature extractors and age estimators on benchmark datasets.