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
On-the-Fly Facial Expression Prediction Using LSTM Encoded Appearance-Suppressed Dynamics
TL;DR: This paper proposed an appearance suppressed dynamics feature, which utilizes a static sequence to suppress the appearance bias and achieved higher recognition rates compared to the state-of-the-art methods on publicly available datasets.
Journal ArticleDOI
Facial Expression Recognition With Confidence Guided Refined Horizontal Pyramid Network
TL;DR: Zhang et al. as discussed by the authors proposed a confidence guided Refined Horizontal Pyramid Network (RHPN) to fully exploit various partial information of a given facial image, which can learn discriminative features with multiple facial granularities Inconsistencies within each granularity are efficiently limited.
Proceedings ArticleDOI
Single-Image Facial Expression Recognition Using Deep 3D Re-Centralization
TL;DR: A single image facial expression recognition method that is robust to face orientation and light conditions is proposed that outperforms the state-of-the-art techniques in both large-scale and small-scale datasets.
Proceedings ArticleDOI
Unsupervised Domain Adaptation with Generative Adversarial Networks for Facial Emotion Recognition
TL;DR: An unsupervised domain adaptation framework with adversarial learning for cross-dataset FER is proposed and performed on three well-known publicly available facial expression databases, showcasing the efficiency of this proposed approach.
Journal ArticleDOI
FERGCN: facial expression recognition based on graph convolution network
TL;DR: Zhang et al. as mentioned in this paper proposed a deep neural network called facial expression recognition based on graph convolution network (FERGCN), which can effectively extract expression information from the face in a complex environment.
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.