Age Estimation for Low-Quality Facial Images: from Separate DCNNs to a Decision Fuser

Abstract

In this paper, we contribute an age estimation method towards dealing with low quality face images. This is a practical and important problem because an image we received may have low resolution or be affected by some noise via transmission. Upon reviewing the literature on facial age estimation, we notice that few articles tackle this low quality image based facial age estimation problem. In our framework, we propose a newly designed deep convolutional neural networks architecture, consisting of five major steps. Firstly, a data augmentation step is utilized to ease the training procedure. Secondly, we propose to use a super-resolution method to enhance the input images. Thirdly, we use a deep network to conduct gender grouping. Fourthly, two recently proposed deep networks are modified with depthwise separable convolutions to perform age estimation within male and female groups. Finally, a fusion procedure is added to further boost age estimation accuracy. In the experiment, we use two benchmark datasets, IMDB-WIKI and MORPH-II, to verify our proposed method and also show a significantly performance improvement over some state-of-the-art methods.