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
Deep Margin-Sensitive Representation Learning for Cross-Domain Facial Expression Recognition
TL;DR: Zhang et al. as mentioned in this paper proposed a novel Deep Margin-Sensitive Representation Learning (DMSRL) framework, which can extract multi-level discriminative features during sematic-aware domain adaptation.
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MVFNet: A multi-view fusion network for pain intensity assessment in unconstrained environment
Ashish Semwal,Narendra D. Londhe +1 more
TL;DR: In this work, decision level fusion of three distinct features, i.e., data-driven RGB features, entropy based texture features, and complementary features learned from both RGB and texture data are utilized to improve the generalization of the proposed pain assessment system.
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Intensity enhancement via GAN for multimodal face expression recognition
TL;DR: Zhang et al. as mentioned in this paper proposed a novel Generative Adversarial Network (GAN) based multimodal approach to face expression recognition (FER) on low expression intensity and achieved state-of-the-art performance for both the circumstance of low expression intensities and general FER scenarios.
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Geometry-Aware Facial Expression Recognition via Attentive Graph Convolutional Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a geometry-aware FER framework to boost the FER performance based on both the geometric and appearance knowledge, which adopts a graph convolutional network to fully explore the structural information of the facial components behind different expressions.
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Quaternion Capsule Neural Network With Region Attention for Facial Expression Recognition in Color Images
TL;DR: Wang et al. as discussed by the authors proposed a quaternion capsule neural network (Q-CapsNet) with region attention mechanism for facial expression recognition in color images, which adopts the concept of quaternions to the Capsule Neural Network and further uses an attention mechanism to focus on the facial region of interests (ROIs).
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.