Latent sparse transfer subspace learning for cross-corpus facial expression recognition

2021 ◽  
pp. 103121
Author(s):  
Wenjing Zhang ◽  
Peng Song ◽  
Dongliang Chen ◽  
Weijian Zhang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Subspace Learning ◽  
Expression Recognition

scholarly journals Robust Transferable Subspace Learning for Cross-Corpus Facial Expression Recognition

2020 ◽  
Vol E103.D (10) ◽  
pp. 2241-2245
Author(s):  
Dongliang CHEN ◽  
Peng SONG ◽  
Wenjing ZHANG ◽  
Weijian ZHANG ◽  
Bingui XU ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Subspace Learning ◽  
Expression Recognition

Combining the Kernel Collaboration Representation and Deep Subspace Learning for Facial Expression Recognition

2018 ◽  
Vol 27 (08) ◽  
pp. 1850121 ◽  
Author(s):  
Zhe Sun ◽  
Zheng-Ping Hu ◽  
Raymond Chiong ◽  
Meng Wang ◽  
Wei He
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Subspace Learning ◽  
Superior Performance ◽  
Expression Recognition ◽  
Learning Networks ◽  
Linear Discriminant ◽  
High Level ◽  
Relationship Of

Recent research has demonstrated the effectiveness of deep subspace learning networks, including the principal component analysis network (PCANet) and linear discriminant analysis network (LDANet), since they can extract high-level features and better represent abstract semantics of given data. However, their representation does not consider the nonlinear relationship of data and limits the use of features with nonlinear metrics. In this paper, we propose a novel architecture combining the kernel collaboration representation with deep subspace learning based on the PCANet and LDANet for facial expression recognition. First, the PCANet and LDANet are employed to learn abstract features. These features are then mapped to the kernel space to effectively capture their nonlinear similarities. Finally, we develop a simple yet effective classification method with squared [Formula: see text]-regularization, which improves the recognition accuracy and reduces time complexity. Comprehensive experimental results based on the JAFFE, CK[Formula: see text], KDEF and CMU Multi-PIE datasets confirm that our proposed approach has superior performance not just in terms of accuracy, but it is also robust against block occlusion and varying parameter configurations.

Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets

2011 ◽  
Vol 24 (8) ◽  
pp. 814-823 ◽  
Author(s):  
Anastasios Maronidis ◽  
Dimitris Bolis ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Subspace Learning ◽  
Expression Recognition ◽  
Training Sets

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

2021 ◽  
Vol 10 ◽  
Author(s):  
Cigdem Turan ◽  
Rui Zhao ◽  
Kin-Man Lam ◽  
Xiangjian He
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Manifold Learning ◽  
Facial Expression Recognition ◽  
Subspace Learning ◽  
Loss Functions ◽  
Expression Recognition ◽  
Learning Methods ◽  
Locality Preserving ◽  
New Perspective

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.

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