We describe a deep learning based method for estimating 3D facial expression
coefficients. Unlike previous work, our process does not relay on facial
landmark detection methods as a proxy step. Recent methods have shown that a
CNN can be trained to regress accurate and discriminative 3D morphable model
(3DMM) representations, directly from image intensities. By foregoing facial
landmark detection, these methods were able to estimate shapes for occluded
faces appearing in unprecedented in-the-wild viewing conditions. We build on
those methods by showing that facial expressions can also be estimated by a
robust, deep, landmark-free approach. Our ExpNet CNN is applied directly to the
intensities of a face image and regresses a 29D vector of 3D expression
coefficients. We propose a unique method for collecting data to train this
network, leveraging on the robustness of deep networks to training label noise.
We further offer a novel means of evaluating the accuracy of estimated
expression coefficients: by measuring how well they capture facial emotions on
the CK+ and EmotiW-17 emotion recognition benchmarks. We show that our ExpNet
produces expression coefficients which better discriminate between facial
emotions than those obtained using state of the art, facial landmark detection
techniques. Moreover, this advantage grows as image scales drop, demonstrating
that our ExpNet is more robust to scale changes than landmark detection
methods. Finally, at the same level of accuracy, our ExpNet is orders of
magnitude faster than its alternatives.

本研究提出一种基于深度学习的新方法用于估计 3D 面部表情系数，该方法不依赖于人脸关键点检测的代理步骤，而是直接对面部图像强度进行处理，采用适应训练数据噪声的独特方法进行网络训练，通过基准测试表明所提出的方法能更准确地区分面部情感表达。