An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions
| Autor: | Pablo Barros, Stefan Wermter, Henrique Siqueira, Sven Magg |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Facial expression Social robot business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) 05 social sciences Feature extraction Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Machine learning computer.software_genre Facial recognition system 050105 experimental psychology ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Robot Leverage (statistics) 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Artificial intelligence business computer |
| Zdroj: | IROS 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
| Popis: | Social robots able to continually learn facial expressions could progressively improve their emotion recognition capability towards people interacting with them. Semi-supervised learning through ensemble predictions is an efficient strategy to leverage the high exposure of unlabelled facial expressions during human-robot interactions. Traditional ensemble-based systems, however, are composed of several independent classifiers leading to a high degree of redundancy, and unnecessary allocation of computational resources. In this paper, we proposed an ensemble based on convolutional networks where the early layers are strong low-level feature extractors, and their representations shared with an ensemble of convolutional branches. This results in a significant drop in redundancy of low-level features processing. Training in a semi-supervised setting, we show that our approach is able to continually learn facial expressions through ensemble predictions using unlabelled samples from different data distributions. |
| Databáze: | OpenAIRE |
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