Visual Media Laboratory, Department of Information Science, Tokyo City University, Tokyo 1588557, Japan.
Sensors (Basel). 2022 May 31;22(11):4171. doi: 10.3390/s22114171.
Age estimation from human faces is an important yet challenging task in computer vision because of the large differences between physical age and apparent age. Due to the differences including races, genders, and other factors, the performance of a learning method for this task strongly depends on the training data. Although many inspiring works have focused on the age estimation of a single human face through deep learning, the existing methods still have lower performance when dealing with faces in videos because of the differences in head pose between frames, which can lead to greatly different results. In this paper, a combined system of age estimation and head pose estimation is proposed to improve the performance of age estimation from faces in videos. We use deep regression forests (DRFs) to estimate the age of facial images, while a multiloss convolutional neural network is also utilized to estimate the head pose. Accordingly, we estimate the age of faces only for head poses within a set degree threshold to enable value refinement. First, we divided the images in the Cross-Age Celebrity Dataset (CACD) and the Asian Face Age Dataset (AFAD) according to the estimated head pose degrees and generated separate age estimates for images with different poses. The experimental results showed that the accuracy of age estimation from frontal facial images was better than that for faces at different angles, thus demonstrating the effect of head pose on age estimation. Further experiments were conducted on several videos to estimate the age of the same person with his or her face at different angles, and the results show that our proposed combined system can provide more precise and reliable age estimates than a system without head pose estimation.
基于人脸的年龄估计是计算机视觉中的一项重要但具有挑战性的任务，因为实际年龄和表观年龄之间存在很大差异。由于种族、性别和其他因素的差异，该任务的学习方法的性能强烈依赖于训练数据。尽管许多有启发性的工作都集中在通过深度学习对单个人脸的年龄估计上，但由于帧间头部姿势的差异，现有方法在处理视频中的人脸时性能仍然较低，这可能导致结果大不相同。在本文中，提出了一种结合年龄估计和头部姿势估计的系统，以提高视频中人脸的年龄估计性能。我们使用深度回归森林（DRFs）来估计面部图像的年龄，同时还使用了多损失卷积神经网络来估计头部姿势。因此，我们仅对头部姿势在设定的角度阈值内的人脸进行年龄估计，以实现值的细化。首先，我们根据估计的头部姿势角度对 Cross-Age Celebrity Dataset（CACD）和 Asian Face Age Dataset（AFAD）中的图像进行了划分，并为不同姿势的图像生成了单独的年龄估计值。实验结果表明，正面人脸图像的年龄估计精度优于不同角度的人脸，从而证明了头部姿势对年龄估计的影响。进一步在几个视频上进行了实验，以估计同一人脸在不同角度的年龄，结果表明，我们提出的联合系统可以提供比没有头部姿势估计的系统更精确和可靠的年龄估计。
