CorrFeat

Correlation-based feature extraction algorithm using skin conductance and pupil diameter for emotion recognition

Conference paper (2019)

Authors

T. Zhang Centrum Wiskunde & Informatica (CWI),
Abdallah El Ali Centrum Wiskunde & Informatica (CWI)
Chen Wang Xinhuanet
Xintong Zhu Xinhuanet
Pablo Cesar Centrum Wiskunde & Informatica (CWI),
Machine learning Emotion recognition Pupil diameter MAHNOB-HCI database Skin conductance response
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Published Date

2019

Language

English

Reuse Rights

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Abstract

To recognize emotions using less obtrusive wearable sensors, we present a novel emotion recognition method that uses only pupil diameter (PD) and skin conductance (SC). Psychological studies show that these two signals are related to the attention level of humans exposed to visual stimuli. Based on this, we propose a feature extraction algorithm that extract correlation-based features for participants watching the same video clip. To boost performance given limited data, we implement a learning system without a deep architecture to classify arousal and valence. Our method outperforms not only state-of-art approaches, but also widely-used traditional and deep learning methods.

Files

CorffFeat.pdf
(.pdf | 1.16 Mb)