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首页> 外文期刊>Journal of visual communication & image representation >Facial expression recognition using dual dictionary learning
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Facial expression recognition using dual dictionary learning

机译：使用双重字典学习的面部表情识别

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In this paper, a novel method is proposed for Facial Expression Recognition (FER) using dictionary learning to learn both identity and expression dictionaries simultaneously. Accordingly, an automatic and comprehensive feature extraction method is proposed. The proposed method accommodates real-valued scores to a probability of what percent of the given Facial Expression (FE) is present in the input image. To this end, a dual dictionary learning method is proposed to learn both regression and feature dictionaries for FER. Then, two regression classification methods are proposed using a regression model formulated based on dictionary learning and two known classification methods including Sparse Representation Classification (SRC) and Collaborative Representation Classification (CRC). Convincing results are acquired for FER on the CK+, CK, MMI and JAFFE image databases compared to several state-of-the-arts. Also, promising results are obtained from evaluating the proposed method for generalization on other databases. The proposed method not only demonstrates excellent performance by obtaining high accuracy on all four databases but also outperforms other state-of-the-art approaches. (C) 2017 Elsevier Inc. All rights reserved.
机译：在本文中，提出了一种新的面部表情识别方法，该方法通过字典学习同时学习身份和表达词典。因此，提出了一种自动，全面的特征提取方法。所提出的方法将实值得分调整为输入图像中存在给定面部表情（FE）百分比的概率。为此，提出了一种双重字典学习方法来学习FER的回归字典和特征字典。然后，使用基于字典学习的回归模型和两种已知的分类方法（包括稀疏表示分类（SRC）和协作表示分类（CRC）），提出了两种回归分类方法。与几种最新技术相比，在CK +，CK，MMI和JAFFE图像数据库上获得了令人信服的FER结果。此外，通过评估所提出的方法在其他数据库上的推广也可以得到令人鼓舞的结果。所提出的方法不仅通过在所有四个数据库上获得较高的准确性来展示出色的性能，而且还优于其他最新技术。 （C）2017 Elsevier Inc.保留所有权利。

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