Photorealistic facial expression synthesis from single face image can be
widely applied to face recognition, data augmentation for emotion recognition
or entertainment. This problem is challenging, in part due to a paucity of
labeled facial expression data, making it difficult for algorithms to
disambiguate changes due to identity and changes due to expression. In this
paper, we propose the conditional difference adversarial autoencoder, CDAAE,
for facial expression synthesis. The CDAAE takes a facial image of a previously
unseen person and generates an image of that human face with a target emotion
or facial action unit label. The CDAAE adds a feedforward path to an
autoencoder structure connecting low level features at the encoder to features
at the corresponding level at the decoder. It handles the problem of
disambiguating changes due to identity and changes due to facial expression by
learning to generate the difference between low-level features of images of the
same person but with different facial expressions. The CDAAE structure can be
used to generate novel expressions by combining and interpolating between
facial expressions/action units within the training set. Our experimental
results demonstrate that the CDAAE can preserve identity information when
generating facial expression for unseen subjects more faithfully than previous
approaches. This is especially advantageous when training with small databases.

本文提出了一种条件差异对抗自编码器（CDAAE）的方法，用于从单张面部图像中合成逼真的面部表情，该方法可以解决因没有标记的面部表情数据而造成的身份和表情变化的消歧问题，同时能够在生成表情的同时保留身份信息，并可用于情感识别和数据扩增等方面。