# Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition

arXiv: https://arxiv.org/abs/2204.12624 · HTML: https://mlsanity.com/paper/2204.12624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Learning Social Relation Traits from Face Images](https://mlsanity.com/paper/1509.03936/) (2015)
- [Deep Neural Network Ensembles against Deception: Ensemble Diversity, Accuracy and Robustness](https://mlsanity.com/paper/1908.11091/) (2019)
- [Eyemotion: Classifying facial expressions in VR using eye-tracking cameras](https://mlsanity.com/paper/1707.07204/) (2017)
- [Deep Self-taught Learning for Remote Sensing Image Classification](https://mlsanity.com/paper/1710.07096/) (2017)

## Abstract (arXiv)

This work describes different strategies to generate unsupervised representations obtained through the concept of self-taught learning for facial emotion recognition (FER). The idea is to create complementary representations promoting diversity by varying the autoencoders' initialization, architecture, and training data. SVM, Bagging, Random Forest, and a dynamic ensemble selection method are evaluated as final classification methods. Experimental results on Jaffe and Cohn-Kanade datasets using a leave-one-subject-out protocol show that FER methods based on the proposed diverse representations compare favorably against state-of-the-art approaches that also explore unsupervised feature learning.