Subspace learning for facial expression recognition: an overview and a new perspective

IF 3.2 Q1 Computer Science
Cigdem Turan, Rui Zhao, K. Lam, Xiangjian He
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引用次数: 5

Abstract

For image recognition, an extensive number of subspace-learning methods have been proposed to overcome the high-dimensionality problem of the features being used. In this paper, we first give an overview of the most popular and state-of-the-art subspace-learning methods, and then, a novel manifold-learning method, named soft locality preserving map (SLPM), is presented. SLPM aims to control the level of spread of the different classes, which is closely connected to the generalizability of the learned subspace. We also do an overview of the extension of manifold learning methods to deep learning by formulating the loss functions for training, and further reformulate SLPM into a soft locality preserving (SLP) loss. These loss functions are applied as an additional regularization to the learning of deep neural networks. We evaluate these subspace-learning methods, as well as their deep-learning extensions, on facial expression recognition. Experiments on four commonly used databases show that SLPM effectively reduces the dimensionality of the feature vectors and enhances the discriminative power of the extracted features. Moreover, experimental results also demonstrate that the learned deep features regularized by SLP acquire a better discriminability and generalizability for facial expression recognition.
面部表情识别的子空间学习:综述与新视角
对于图像识别,已经提出了大量的子空间学习方法来克服所使用的特征的高维问题。在本文中,我们首先概述了最流行和最先进的子空间学习方法,然后提出了一种新的流形学习方法,称为软局域保持映射(SLPM)。SLPM的目的是控制不同类的扩散程度,这与学习到的子空间的可泛化性密切相关。我们还概述了流形学习方法在深度学习中的扩展,通过制定用于训练的损失函数,并进一步将SLPM重新表述为软局域保持(SLP)损失。这些损失函数作为一种额外的正则化应用于深度神经网络的学习。我们评估了这些子空间学习方法,以及它们在面部表情识别上的深度学习扩展。在4个常用数据库上的实验表明,SLPM有效地降低了特征向量的维数,提高了提取特征的判别能力。此外,实验结果还表明,学习到的深度特征经SLP正则化后,对面部表情识别具有较好的判别性和泛化性。
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
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