Distribution Highlighted Reference-based Label Distribution Learning for Facial Age Estimation

Estimating age from facial images is a fundamental task. In this task, age labels have ambiguity because faces of the same individual across similar ages are often difficult to distinguish. To model this ambiguity, label distribution learning (LDL) trains a deep neural network (DNN) using a label distribution, i.e., the probability that an image belongs to each age, instead of a single age label. However, the heuristic constraints utilized for LDL often fail to accurately model the label ambiguity. Therefore, we propose a novel LDL method called distribution highlighted reference-based LDL (DHRL), which introduces an input-dependent constraint by utilizing a reference DNN pre-trained with any LDL method and minimizing the gap between the reference and target DNNs' outputs. DHRL incorporates two techniques to highlight the label ambiguity hidden in the pre-trained reference DNN's output: noisy augmentation-based ensembling (NAE) and different scale multi-temperature (DSM). NAE inputs noisy images to the reference DNN and provides an ensemble effect by averaging all the outputs. DSM sets multiple temperatures simultaneously in the gap minimization between the two DNNs' outputs. Experimental results indicate that our method achieves state-of-the-art performance across various datasets and conditions.