Transfer learning approach to multiclass classification of child facial expressions

Abstract

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a dearth of literature in child facial expression analysis. Recent advances in transfer learning methods have enabled the use of deep learning architectures, trained on adult facial expression images, to be tuned for classifying child facial expressions with limited training samples. The network will learn generic facial expression patterns from adult expressions which can be fine-tuned to capture representative features of child facial expressions. This work proposes a transfer learning approach for multi-class classification of the seven prototypical expressions including the ‘neutral’ expression in children using a recently published child facial expression data set. This work holds promise to facilitate the development of technologies that focus on children and monitoring of children throughout their developmental stages to detect early symptoms related to developmental disorders, such as Autism Spectrum Disorder (ASD).