Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features

arXiv:2209.08614v2 Announce Type: replace-cross
Abstract: Imaging of facial affects may be used to measure psychophysiological attributes of children through their adulthood for applications in education, healthcare, and entertainment, among others. Deep convolutional neural networks show promising results in classifying facial expressions of adults. However, classifier models trained with adult benchmark data are unsuitable for learning child expressions due to discrepancies in psychophysical development. Similarly, models trained with child data perform poorly in adult expression classification. We propose domain adaptation to …

abstract applications arxiv benchmark child children classifier convolutional neural networks cs.cv cs.lg education entertainment features healthcare however imaging landmark networks neural networks results show through type