FePh: An Annotated Facial Expression Dataset for the RWTH-PHOENIX-Weather 2014 Dataset

Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression dataset in the context of sign language are still scarce resources. In this manuscript, we introduce a continuous sign language facial expression dataset, comprising over $3000$ annotated images of the RWTH-PHOENIX-Weather 2014 development set. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image's facial expression could not be described by any of the aforementioned emotions. Although we provide FePh in the context of facial expression and sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems. The dataset will be publicly available.