Abstract:
For a child to incorporate himself into the society as a productive individual, it is vital that his
emotional development is sound. In recent years, the emergence of several real-world
applications has shifted the focus of technology towards enabling machines to decode and
understand the emotional signals of human beings. However, in research, very often emotion
recognition is limited to adults, not considering the fact that children can develop an awareness
of various emotions at a very early stage, thus, the ability of machines, to distinguish various
facial expressions of children, still needs to be explored. In the absence of a standardized
database, it yet remains a challenge. Thus a set of benchmarks are required to establish a
standardized comparison. In this paper, a system based on convolutional neural networks has
been proposed to automatically recognize children‟s expressions. For this purpose, a video
dataset for Children‟s Spontaneous facial Expressions (LIRIS-CSE) has been used. In our
proposed system, pre-trained Convolutional Neural Network (CNN) such as VGG19, VGG16,
and Resnet50 have been deployed as feature extractors and then models such as Support Vector
Machine (SVM) and Decision Tree (DT) have been used for classification. We have tested their
strength with various experimental setups such as 80-20% split, K-Fold Cross Validation (KFold CV), and Leave One out Cross-Validation (LOOCV), for both image-based and videobased classification approaches. Our research has achieved a promising classification accuracy
of 99% for image-based classification via features of all three networks with SVM using 80-20%
split and K-Fold CV. Video-based classification results have been reported as well, where we
have managed to achieve 94% accuracy via features of VGG19 with SVM using LOOCV. Our
achieved results are better as compared to the original work, where they have achieved an
average image-based classification accuracy of 75% on their designed LIRIS-CSE dataset.
