Proceedings ArticleDOI
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild
Shan Li,Weihong Deng,Junping Du +2 more
- pp 2584-2593
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TLDR
A new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters, is proposed.Abstract:
Past research on facial expressions have used relatively limited datasets, which makes it unclear whether current methods can be employed in real world. In this paper, we present a novel database, RAF-DB, which contains about 30000 facial images from thousands of individuals. Each image has been individually labeled about 40 times, then EM algorithm was used to filter out unreliable labels. Crowdsourcing reveals that real-world faces often express compound emotions, or even mixture ones. For all we know, RAF-DB is the first database that contains compound expressions in the wild. Our cross-database study shows that the action units of basic emotions in RAF-DB are much more diverse than, or even deviate from, those of lab-controlled ones. To address this problem, we propose a new DLP-CNN (Deep Locality-Preserving CNN) method, which aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatters. The benchmark experiments on the 7-class basic expressions and 11-class compound expressions, as well as the additional experiments on SFEW and CK+ databases, show that the proposed DLP-CNN outperforms the state-of-the-art handcrafted features and deep learning based methods for the expression recognition in the wild.read more
Citations
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Journal ArticleDOI
Deep Facial Expression Recognition: A Survey
Shan Li,Weihong Deng +1 more
TL;DR: A comprehensive survey on deep facial expression recognition (FER) can be found in this article, including datasets and algorithms that provide insights into the intrinsic problems of deep FER, including overfitting caused by lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias.
Journal ArticleDOI
Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism
TL;DR: Visualization results demonstrate that, compared with the CNN without Gate Unit, ACNNs are capable of shifting the attention from the occluded patches to other related but unobstructed ones and outperform other state-of-the-art methods on several widely used in thelab facial expression datasets under the cross-dataset evaluation protocol.
Journal ArticleDOI
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition
Shan Li,Weihong Deng +1 more
TL;DR: A new deep locality-preserving convolutional neural network (DLP-CNN) method that aims to enhance the discriminative power of deep features by preserving the locality closeness while maximizing the inter-class scatter is proposed.
Proceedings ArticleDOI
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
TL;DR: A new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs) is presented, and the superiority of the proposed method over the state-of-the-art approaches is proved.
Journal ArticleDOI
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
TL;DR: Zhang et al. as mentioned in this paper proposed a region attention network (RAN) to adaptively capture the importance of facial regions for occlusion and pose variant FER by aggregating and embedding varied number of region features produced by a backbone convolutional neural network into a compact fixed-length representation.
References
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