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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Dynamic Facial Expression Generation on Hilbert Hypersphere With Conditional Wasserstein Generative Adversarial Nets
TL;DR: In this paper , a manifold-valued Wasserstein generative adversarial network (GAN) was proposed for dynamic facial expression generation on the hypersphere, which can learn the distribution of facial expression dynamics of different classes, from which they synthesize new facial expression motions.
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Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography
TL;DR: The Expressure system that performs surface pressure mechanomyography on the forehead using an array of textile pressure sensors that is not dependent on specific placement or attachment to the skin is presented.
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Detecting facial emotions using normalized minimal feature vectors and semi-supervised twin support vector machines classifier
TL;DR: This paper proposes the 13 minimal feature vectors that have high variance among the entire feature vectors are sufficient to identify the six basic emotions and indicates them to be more reliable than existing models.
Posted Content
Video-based Facial Expression Recognition using Graph Convolutional Networks
TL;DR: A Graph Convolutional Network layer is introduced into a common CNN-RNN based model for video-based FER task, and it is the first time to use GCN in FER, and the experimental results demonstrate that the method has superior performance to existing methods.
Posted Content
Automatic Recognition of Facial Displays of Unfelt Emotions
Kaustubh Kulkarni,Ciprian A. Corneanu,Ikechukwu Ofodile,Sergio Escalera,Xavier Baró,Sylwia Hyniewska,Jüri Allik,Gholamreza Anbarjafari +7 more
TL;DR: Results show that the proposed SASE-FE dataset, the first dataset of facial expressions that are either congruent or incongruent with underlying emotion states, improves state of the art results on CK+ and OULU-CASIA datasets for video emotion recognition, and achieves competitive results when classifying facial action units on BP4D datase.
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.