  Facial expressions are important parts of both gesture and sign language
recognition systems. Despite the recent advances in both fields, annotated
facial expression datasets in the context of sign language are still scarce
resources. In this manuscript, we introduce an annotated sequenced facial
expression dataset in the context of sign language, comprising over $3000$
facial images extracted from the daily news and weather forecast of the public
tv-station PHOENIX. 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 as a facial expression dataset of signers in sign
language, it has a wider application in gesture recognition and Human Computer
Interaction (HCI) systems.
