Facial expression recognition is the task of classifying the expressions on face images into various categories such as anger, fear, surprise, sadness, happiness and so on.
( Image credit: DeXpression )
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Crowd sourcing has become a widely adopted scheme to collect ground truth labels.
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators.
The feature embedding module is a deep Convolutional Neural Network (CNN) which embeds face images into feature vectors.
Ranked #1 on Facial Expression Recognition on CK+
Extensive experiments show that our RAN and region biased loss largely improve the performance of FER with occlusion and variant pose.
Ranked #1 on Facial Expression Recognition on SFEW
On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size.
Ranked #1 on Facial Expression Recognition on Oulu-CASIA
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Ranked #1 on Facial Expression Recognition on FER2013 (using extra training data)
Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error on the AffectNet and FER+ datasets, reach human-level performance, and outperform state-of-the-art methods on facial expression recognition in the wild using emotion and affect concepts.
Ranked #1 on Facial Expression Recognition on FER+ (using extra training data)
In this work, we explore the benefits of using a man- ifold network structure for covariance pooling to improve facial expression recognition.
Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years.
Ranked #2 on Facial Expression Recognition on MMI
Dense 3D facial motion capture from only monocular in-the-wild pairs of RGB images is a highly challenging problem with numerous applications, ranging from facial expression recognition to facial reenactment.