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Unsupervised facial expression recognition using domain adaptation based dictionary learning approach

机译：使用基于领域自适应的字典学习方法进行无监督的面部表情识别

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摘要

Over the past years, dictionary learning (DL) based methods have achieved excellent performance in facial expression recognition (FER), where training and testing data are usually presumed to have the same distributions. But in the practical scenarios, this assumption is often broken, especially when training and testing data come from different databases, a.k.a. the cross-database FER problem. In this paper, we focus on the unsupervised cross-domain FER problem where all the samples in target domain are completely unannotated. To address this problem, we propose an unsupervised domain adaptive dictionary learning (UDADL) model to bridge source domain and target domain by learning a shared dictionary. The encoding of the two domains on this dictionary are constrained to be mutually embedded on each other. To bypass the solution complexity, we borrow an analysis dictionary to seek for approximate solutions as the latent variable to favor sub-solvers to be analyzed. To evaluate the performance of the proposed UDADL model, we conduct extensive experiments on the widely used Multi-PIE and BU-3DFE databases. The experimental results demonstrated that the proposed UDADL method outperforms recent domain adaptation FER methods and achieved the state-of-the-art performance. (c) 2018 Published by Elsevier B.V.
机译：在过去的几年中，基于字典学习（DL）的方法在面部表情识别（FER）中取得了出色的性能，其中训练和测试数据通常被认为具有相同的分布。但是在实际情况下，这种假设通常会被打破，尤其是当训练和测试数据来自不同的数据库时，也就是跨数据库FER问题。在本文中，我们关注于无监督的跨域FER问题，其中目标域中的所有样本都完全未注释。为了解决这个问题，我们提出了一种无监督域自适应字典学习（UDADL）模型，通过学习共享字典来桥接源域和目标域。该词典上两个域的编码被限制为相互嵌入。为了绕开解决方案的复杂性，我们借用分析词典来寻找近似解决方案作为潜在变量，以偏爱要分析的子解决方案。为了评估建议的UDADL模型的性能，我们在广泛使用的Multi-PIE和BU-3DFE数据库上进行了广泛的实验。实验结果表明，所提出的UDADL方法优于最近的领域自适应FER方法，并达到了最新的性能。 （c）2018年由Elsevier B.V.

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