论文标题
分析面部表达识别的半监督方法
Analysis of Semi-Supervised Methods for Facial Expression Recognition
论文作者
论文摘要
培训深层神经网络以识别图像识别通常需要大规模的人体注释数据。为了减少深度神经溶液对标记数据的依赖，在文献中提出了最先进的半监督方法。但是，在面部表达识别（FER）领域，使用这种半监督方法非常罕见。在本文中，我们介绍了一项关于最近提出的在FER背景下最先进的半监督学习方法的全面研究。我们对八种半监督学习方法进行了比较研究，即Pi-Model，Pseudo-Label，均值老师，VAT，MixMatch，MixMatch，RemixMatch，UDA和FixMatch在三个FER数据集（FER13，RAF-DB和Actionnet）上，当使用了各种标记的样品时。我们还将这些方法的性能与完全监督的培训进行了比较。我们的研究表明，当培训现有的半监督方法时，每类标记的样品只有250个标记的样本可以产生可比的性能，与在完整标记的数据集中训练的完全监督的方法。为了促进该领域的进一步研究，我们在：https：//github.com/shuvenduroy/ssl_fer上公开提供代码
Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature. Nonetheless, the use of such semi-supervised methods has been quite rare in the field of facial expression recognition (FER). In this paper, we present a comprehensive study on recently proposed state-of-the-art semi-supervised learning methods in the context of FER. We conduct comparative study on eight semi-supervised learning methods, namely Pi-Model, Pseudo-label, Mean-Teacher, VAT, MixMatch, ReMixMatch, UDA, and FixMatch, on three FER datasets (FER13, RAF-DB, and AffectNet), when various amounts of labeled samples are used. We also compare the performance of these methods against fully-supervised training. Our study shows that when training existing semi-supervised methods on as little as 250 labeled samples per class can yield comparable performances to that of fully-supervised methods trained on the full labeled datasets. To facilitate further research in this area, we make our code publicly available at: https://github.com/ShuvenduRoy/SSL_FER
