Recognition of Children's Facial Expressions Using Deep Learned Features

The emotional well-being of a child is crucial for their successful integration into society as a productive individual. While technology has made significant strides in enabling machines to decipher human emotional signals, current research in emotion recognition primarily prioritizes adults, disregarding the fact that children develop emotional awareness at an early stage. This highlights the need to explore how machines can recognize facial expressions in children, although the absence of a standardized database poses a challenge. In this study, we propose a system that employs Convolutional-Neural-Network (CNN)-based models, such as VGG19, VGG16, and Resnet50, as feature extractors, and Support Vector Machine (SVM) and Decision Tree (DT) for classification, to automatically recognize children's expressions using a video dataset, namely Children's Spontaneous Facial Expressions (LIRIS-CSE). Our system is evaluated through various experimental setups, including 80-20% split, K-Fold Cross-Validation (K-Fold CV), and leave one out cross-validation (LOOCV), for both image-based and video-based classification. Remarkably, our research achieves a promising classification accuracy of 99% for image-based classification, utilizing features from all three networks with SVM using 80-20% split and K-Fold CV. For video-based classification, we achieve 94% accuracy using features from VGG19 with SVM using LOOCV. These results surpass the performance of the original work, which reported an average image-based classification accuracy of 75% on their LIRIS-CSE dataset. The favorable outcomes obtained from our research can pave the way for the practical application of our proposed emotion recognition methodology in real-world scenarios.