Unsupervised learning for physiological signals in real-life emotion recognition using wearables

Kune, Dominika

doi:10.1109/aciiw57231.2022.10086004

Cited by 1 publication

(1 citation statement)

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“…These improvements facilitate emotion recognition from both facial expression analysis and physiological perspectives, accommodating variances that might occur with specific emotions. While several studies have utilized the physiological signals from the Emognition dataset to classify emotions (e.g., [25][26][27][28] ), there has been relatively little research examining the facial expression data within the same dataset for emotion recognition and classification (e.g., 29 . This gap highlights a key area for further investigation, aiming to fully leverage the dataset's capabilities in enhancing FER technologies.…”

Section: Related Workmentioning

confidence: 99%

Image-based facial emotion recognition using convolutional neural network on emognition dataset

Agung,

Rifai,

Wijayanto

2024

Sci Rep

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Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limited range of expressions. This study expands the use of deep learning for facial emotion recognition (FER) based on Emognition dataset that includes ten target emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness, and neutral. A series of data preprocessing was carried out to convert video data into images and augment the data. This study proposes Convolutional Neural Network (CNN) models built through two approaches, which are transfer learning (fine-tuned) with pre-trained models of Inception-V3 and MobileNet-V2 and building from scratch using the Taguchi method to find robust combination of hyperparameters setting. The proposed model demonstrated favorable performance over a series of experimental processes with an accuracy and an average F1-score of 96% and 0.95, respectively, on the test data.

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