Abstract
Facial pose estimation has gained a lot of attentions in many practical applications, such as human-robot interaction, gaze estimation and driver monitoring. Meanwhile, end-to-end deep learning-based facial pose estimation is becoming more and more popular. However, facial pose estimation suffers from a key challenge: the lack of sufficient training data for many poses, especially for large poses. Inspired by the observation that the faces under close poses look similar, we reformulate the facial pose estimation as a label distribution learning problem, considering each face image as an example associated with a Gaussian label distribution rather than a single label, and construct a convolutional neural network which is trained with a multi-loss function on AFLW dataset and 300WLP dataset to predict the facial poses directly from color image. Extensive experiments are conducted on several popular benchmarks, including AFLW2000, BIWI, AFLW and AFW, where our approach shows a significant advantage over other state-of-the-art methods.
Abstract (translated)
人脸姿态估计在许多实际应用中得到了广泛的关注，如人机交互、视线估计和驾驶员监控等。同时，基于端到端深度学习的人脸姿态估计越来越流行。然而，面部姿势估计面临着一个关键的挑战：缺乏足够的训练数据来进行许多姿势，尤其是大型姿势。受近距离姿态下人脸相似性观察的启发，我们将人脸姿态估计重新表述为标签分布学习问题，将每个人脸图像作为一个与高斯标签分布相关的例子，而不是单个标签，并构造了一个卷积神经网络，该网络由多个-利用AFLW数据集和300WLP数据集的损失函数直接从彩色图像中预测面部姿势。我们在几个流行的基准上进行了广泛的实验，包括AFLW2000、Biwi、AFLW和AFW，在这些基准上，我们的方法比其他最先进的方法显示出显著的优势。
URL
https://arxiv.org/abs/1904.13102
