Journal ArticleDOI
Facial expression recognition from near-infrared videos
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TLDR
A novel research on a dynamic facial expression recognition, using near-infrared (NIR) video sequences and LBP-TOP feature descriptors and component-based facial features are presented to combine geometric and appearance information, providing an effective way for representing the facial expressions.About:
This article is published in Image and Vision Computing.The article was published on 2011-08-01. It has received 586 citations till now. The article focuses on the topics: Three-dimensional face recognition & Face hallucination.read more
Citations
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Journal ArticleDOI
A discriminative deep association learning for facial expression recognition
Xing Jin,Wenyun Sun,Zhong Jin +2 more
TL;DR: A novel discriminative deep association learning (DDAL) framework is proposed, providing unlabeled data to train the DNNs with the labeled data simultaneously, in a multi-loss deep network based on association learning.
Journal ArticleDOI
Relational Deep Feature Learning for Heterogeneous Face Recognition
TL;DR: A graph-structured module called Relational Graph Module (RGM) that extracts global relational information in addition to general facial features that outperforms other state-of-the-art methods on five HFR databases and demonstrates performance improvement on three backbones.
Proceedings ArticleDOI
Video-based Facial Expression Recognition using Graph Convolutional Networks
TL;DR: Wang et al. as mentioned in this paper introduced a Graph Convolutional Network (GCN) layer into a common CNN-RNN based model for video-based facial expression recognition, which can learn more significant facial expression features which concentrate on certain regions after sharing information between extracted CNN features of nodes.
Journal ArticleDOI
Active AU Based Patch Weighting for Facial Expression Recognition.
TL;DR: The problem of multiclass expression recognition is converted into triplet-wise expression recognition, and a new feature optimization model based on action unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet.
Journal ArticleDOI
Triplet Loss With Multistage Outlier Suppression and Class-Pair Margins for Facial Expression Recognition
TL;DR: Wang et al. as discussed by the authors proposed a new triplet loss based on class-pair margins and multistage outlier suppression for facial expression recognition (FER), which assigned each expression pair with an order-insensitive or two order-aware adaptive margin parameters.
References
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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
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Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
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On combining classifiers
TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
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From few to many: illumination cone models for face recognition under variable lighting and pose
TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.