Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions
Journal
IEEE Transactions on Affective Computing
Date Issued
2023
Author(s)
Abstract
Being able to recognize emotional intensity is a desirable feature for a facial emotional recognition (FER) system. However, the development of such a feature is hindered by the paucity of intensity-labeled data for model training. To ameliorate the situation, the present study proposes using face morphing as a way of data augmentation to synthesize faces that express different degrees of a designated emotion. Such an approach has been successfully validated on humans and machines. Specifically, humans indeed perceived different levels of intensified emotions in these parametrically synthesized faces, and FER systems based on neural networks indeed showed improved sensitivities to intensities of different emotions when additionally trained on the synthesized faces. Overall, the proposed data augmentation method is not only simple and effective but also useful for building FER systems that recognize facial expressions of mixed emotions. CCBYNCND
Subjects
Computational modeling
Data augmentation
Data models
Databases
emotion recognition
Emotion recognition
emotional intensity
face morphing
Face recognition
Faces
facial emotional expressions
Training
Behavioral research
Desirable features
Emotional recognition
Face Morphing
Facial emotions
Facial Expressions
Model training
Type
journal article