Adapting the Interplay between Personalized and Generalized Affect Recognition based on an Unsupervised Neural Framework

Recent emotion recognition models, most of them being based on strongly supervised deep learning solutions, are rather successful in recognizing instantaneous emotion expressions. However, when applied to continuous interactions, these models show aweaker adaptation to a person-specific and long‐term emotion appraisal. In this paper, we present an unsupervised neural framework that improves emotion recognition by learning how to describe continuous affective behavior of individual persons. Our framework is composed of three self‐organizing mechanisms: (1) a recurrent growing layer to cluster general emotion expressions, (2) a set of associative layers, acting as affective memories to model specific emotional behavior of individual persons, (3) and an online learning layer which provides contextual modeling of continuous emotion expressions. We propose different learning strategies to integrate all three mechanisms and to improve the performance on arousal and valence recognition of the OMG‐Emotion dataset. We evaluate our model with a series of experiments ranging from ablation studies assessing the different contributions of each neural component to an objective comparison with state‐of‐the‐art solutions. The results from the evaluations show a good performance on emotion recognition of continuous emotions on monologue videos. Furthermore, we discuss how the model self‐regulates the interplay between generalized and personalized emotion perception and how this influences the model's reliability when recognizing unseen emotion expressions.

@Article{BBW22, 
 	 author =  {Barros, Pablo and Barakova, Emilia and Wermter, Stefan},  
 	 title = {Adapting the Interplay between Personalized and Generalized Affect Recognition based on an Unsupervised Neural Framework}, 
 	 journal = {IEEE Transactions on Affective Computing},
 	 number = {},
 	 volume = {13(3)},
 	 pages = {1349-1365},
 	 year = {2022},
 	 month = {Nov},
 	 publisher = {IEEE},
 	 doi = {10.1109/TAFFC.2020.3002657}, 
 }