论文标题
更全面的面部反转以更有效的表达识别
More comprehensive facial inversion for more effective expression recognition
论文作者
论文摘要
面部表达识别（FER）在计算机视觉的普遍应用中起着重要作用。我们从新的角度重新审视了这个问题，即它是否可以获取有用的表示形式，以改善图像生成过程中的FER性能，并根据FER任务的图像反转机制提出了一种新颖的生成方法，称为倒置FER（IFER）。特别是，我们设计了一种新型的对抗性风格反演变压器（ASIT），以全面提取生成的面部图像的特征。此外，ASIT配备了图像反转鉴别器，该歧视器可以测量源和生成图像之间语义特征的余弦相似性，并受到分布比对损失的约束。最后，我们引入了一个功能调制模块，以融合ASIT的结构代码和潜在代码，以供随后的FER工作。我们在FFHQ和Celeba-HQ等面部数据集上广泛评估ASIT，这表明我们的方法可以实现最先进的面部反转性能。 IFER还可以在面部表达识别数据集（例如RAF-DB，SFEW和AffectNet）中实现竞争成果。代码和型号可在https://github.com/talented-q/ifer-master上找到。
Facial expression recognition (FER) plays a significant role in the ubiquitous application of computer vision. We revisit this problem with a new perspective on whether it can acquire useful representations that improve FER performance in the image generation process, and propose a novel generative method based on the image inversion mechanism for the FER task, termed Inversion FER (IFER). Particularly, we devise a novel Adversarial Style Inversion Transformer (ASIT) towards IFER to comprehensively extract features of generated facial images. In addition, ASIT is equipped with an image inversion discriminator that measures the cosine similarity of semantic features between source and generated images, constrained by a distribution alignment loss. Finally, we introduce a feature modulation module to fuse the structural code and latent codes from ASIT for the subsequent FER work. We extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ, showing that our approach achieves state-of-the-art facial inversion performance. IFER also achieves competitive results in facial expression recognition datasets such as RAF-DB, SFEW and AffectNet. The code and models are available at https://github.com/Talented-Q/IFER-master.
