Facial expression recognition via learning deep sparse autoencoders

By Nianyin Zeng, Hong Zhang, Baoye Song, Weibo Liu, Yurong Li, A. Dobaie. 2018

450 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

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

1. Shan Li
2. Weihong Deng
3. Tong Tong
4. Qinquan Gao
5. Mei Wang
6. Michael J. Black
7. Timo Bolkart
8. Mengying Shu

In Experimental and Cognitive Psychology

1. Weihong Deng
2. Shan Li

Citations in the corpus

• Deep Facial Expression Recognition: A Survey (2018, background): “data can also help enhance the deep network performance [58], [120].”
• Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment (2018, background): “In recent years, deep learning, as a promising machine learning methodology, has made a big leap in identifying and classifying patterns of images (Li et al., 2015; Zeng et al., 2016, 2018).”
• A Unifying Review of Deep and Shallow Anomaly Detection (2020, unknown): “Following ideas of sparse coding [384]–[387], sparse AEs [388], [389] regularize the (possibly higher dimensional, overcomplete) latent code toward sparsity, for example, via L(1) Lasso penalization [390].”