Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks
By Kaihao Zhang, Yongzhen Huang, Yong Du, Liang Wang. IEEE Transactions on Image Processing, 2017
426 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/8eb40d0a0a1339469a05711f532839e8ffd8126c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Weihong Deng
- Dacheng Tao
- Luc Van Gool
- Lin Ma
- Wei David Liu
- Hongdong Li
- Ming-Hsuan Yang
- Xiaogang Wang
In Experimental and Cognitive Psychology
- Weihong Deng
- Shan Li
- Vinod Chandran
- Brijesh K. Verma
- Dian Tjondronegoro
- Kaihao Zhang
- Ligang Zhang
- Dacheng Tao
Citations in the corpus
- Adversarial Spatio-Temporal Learning for Video Deblurring (2018, background): “Deep learning has shown its effectiveness in many computer vision tasks, such as object detection [34], image classification [35]–[38], facial processing [2], [39]–[42] and multimedia analysis [43]–[45].”
- A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “ConvNet learning for FERM adopted from [214]; (f) Deep ConvNet-RNN learning for FER adopted from [215]; (g) Deep adversarial learning for 3D FER adopted from [216].”
- Attention mechanism-based CNN for facial expression recognition (2020, methodology): “After feature description, the features are fed into a classifier, such as SVM [26] and K-nearest Neighbors (KNN) [27], for recognizing different facial expressions.”
- Deep Facial Expression Recognition: A Survey (2018, background): “[73] fused the temporal network PHRNN (discussed in “Landmark Trajectory”) and the spatial networkMSCNN (discussed in Section 4.”
- Geometry-Contrastive GAN for Facial Expression Transfer. (2018, background): “Although contrastive learning has been widely used in recognition tasks [25, 26, 31], potentials of the semantic manifold in latent space haven’t been fully explored, which means that inter-class transitions can be expressed in a continuous representation.”
- IExpressNet: Facial Expression Recognition with Incremental Classes (2020, methodology): “Following the previous works [27, 71, 75] for FER, we select 6 basic expressions as the experimental data of this paper.”
- Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency (2019, methodology): “To address the feature issue, deep saliency models [40]–[43] proposed to use feature representation learned from data by using Convolutional Neural Networks (CNNs) [44], [45].”
- Facial Expression Analysis under Partial Occlusion (2018, background): “…Neural Networks (CNNs) [Chang et al. 2017], Long Short-Term Memory [Rodriguez et al. 2017], and fusion of multiple deep neural networks [Zhang et al. 2017], have gained increasing popularity due to their ability to deliver state-of-the-art performance on FEA (see a recent review in…”