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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Proceedings ArticleDOI
Identity-Adaptive Facial Expression Recognition through Expression Regeneration Using Conditional Generative Adversarial Networks
TL;DR: This paper presents a novel approach (so-called IA-gen) to alleviate the issue of subject variations by regenerating expressions from any input facial images by training conditional generative models to generate six prototypic facial expressions fromany given query face image while keeping the identity related information unchanged.
Journal ArticleDOI
Hard negative generation for identity-disentangled facial expression recognition
TL;DR: A novel FER framework, named identity-disentangled facial expression recognition machine (IDFERM), is proposed, in which the identity is untangled from a query sample by exploiting its difference from its references.
Posted Content
DAiSEE: Towards User Engagement Recognition in the Wild
TL;DR: DAiSEE is introduced, the first multi-label video classification dataset comprising of 9068 video snippets captured from 112 users for recognizing the user affective states of boredom, confusion, engagement, and frustration in the wild.
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Deep generative-contrastive networks for facial expression recognition.
TL;DR: This paper deploys deep neural networks that embed a combination of a generative model, a contrastive model, and a discriminative model with an end-to-end training manner that outperforms the known state-of-the art methods in terms of the recognition accuracy.
Posted Content
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
TL;DR: A novel idea to train an expression recognition network based on static images, using a new distribution function to model the high-level neurons of the expression network and achieves better results than state-of-the-art.
References
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On combining classifiers
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Journal ArticleDOI
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.