Transfer learning based Facial Emotion Detection for Animated Characters

Since 1906 till today animation has been a popular method of storytelling. Animated characters portray unique stories in a more perceivable way by expressing diverse facial expressions. Detection of these emotions has not yet gained as much acclaim as detection of human facial expressions. This research aims to classify and predict emotions of animated characters using Deep Learning Techniques. Images of seven facial expressions namely Anger, Disgust, Fear, Joy, Neutral, Sadness and Surprise are classified using Residual Network (ResNet) and Transfer Learning. In order to conduct research on emotion identification in animated faces, we generated a new dataset with fewer images than the existing dataset [1]. Features from the faces are extracted using Uniform Local Binary Patterns (LBP) and fed to Convolutional Neural Network (CNN) model for classification. In our proposed models, the Transfer Learning-based ResNet50 and ResNet101,were trained on the ImageNet [10] dataset. Among the models ResNet101 achieved the highest detection accuracy of 94% and ResNet50 showed lowest time complexity.