Gender and Age Estimation from Facial Images Based on Multi-Task and Curriculum Learning

This study presents a multi-task deep learning approach for predicting age and gender attributes from facial images, with the aim of obtaining a robust dual classifier. The proposed system uses the pre-trained EfficientNet-B4 model as the feature extractor of the main model and incorporates a two-branch architecture, where the output of the gender classification branch informs the age prediction branch. This means a conditional feature learning with an explicit injection mechanism, by injecting gender information into the age field of the dual-task model, which is one of the novelties of our proposal. A curriculum learning strategy is applied during training to progressively improve the model’s performance using various datasets, such as UTKFace, MORPH-II, and Adience. The proposed multi-phase curriculum learning strategy, which uses both multi-task learning and multi-dataset training, is another novelty of our proposal. Experimental results show that the model achieves high accuracy in both age and gender classification tasks while maintaining low inference latency. Furthermore, the experiments highlighted that the classification accuracy values of the proposed method, both for gender and age, as well as in all datasets used, are close to the best state-of-the-art results, which validates the robustness of the proposed classifier.