SAFER: Situation Aware Facial Emotion Recognition
Mijanur Palash Bharat Bhargava
In this paper, we present SAFER, a novel system for emotion recognition from
facial expressions. It employs state-of-the-art deep learning techniques to
extract various features from facial images and incorporates contextual
information, such as background and location type, to enhance its performance.
The system has been designed to operate in an open-world setting, meaning it
can adapt to unseen and varied facial expressions, making it suitable for
real-world applications. An extensive evaluation of SAFER against existing
works in the field demonstrates improved performance, achieving an accuracy of
91.4% on the CAER-S dataset. Additionally, the study investigates the effect of
novelty such as face masks during the Covid-19 pandemic on facial emotion
recognition and critically examines the limitations of mainstream facial
expressions datasets. To address these limitations, a novel dataset for facial
emotion recognition is proposed. The proposed dataset and the system are
expected to be useful for various applications such as human-computer
interaction, security, and surveillance.