Deep Age Estimation Model Stabilization from Images to Videos

Abstract

Deep learning models for age estimation from a single image have significantly improved the state-of-the-art. However, when deploying a deep age estimation model from images directly to videos, it often suffers from the fluctuation issue, i.e., the estimated age varies a lot for face frames from the same person. To deal with this problem, this work presents a new deep age estimation model specifically designed for video facial age estimation, which produces very stable and accurate age estimation results. The proposed deep architecture for video facial age estimation incorporates a convolutional neural network with an attention mechanism, where the convolutional neural network extracts the facial features, and an attention block aggregates the facial feature vectors into a single feature representation for final age estimation. The whole model is trained by a novel loss function to guarantee both the accuracy of each frame and the stabilization of age estimation results of all the frames. To evaluate the proposed model for video facial age estimation, a new dataset is collected and annotated. Extensive experimental analyses and comparisons demonstrate the effectiveness of the proposed model and the state-of-the-art performances compared to many competing methods.