Deep embedding network for robust age estimation
Estimating age through a single facial image is a classic and challenging topic in computer vision. Since facial images of the same age vary considerably, while those from different ages may look very similar. To address these problems, we propose an end-to-end deep embedding neural network for robust age estimation. Specifically, we jointly use classification loss and triplet-based ranking loss to train a deep embedding network, which maps the input facial images into an embedding metric space where features of the same age are compact and those from different ages are pushed away. Thus the deep embedding network can learn more discriminative features and improves the performance for age estimation. Additionally, to accelerate the convergence of the network, we adopt an online hard negative mining strategy during the triplet loss computation. Experimental results on public datasets MORPH II and FG-NET show the superiority of our approach compared to the state-of-the-art.