Framework for reliable, real-time facial expression recognition for low resolution images

• Facial expressions can be analyzed automatically by mimicking human visual system.
• Proposed descriptor has strong discriminative ability.
• Proposed framework is robust for low resolution images and spontaneous expressions.
• Proposed framework generalizes well on unseen data.
• The proposed framework can be used for real-time applications.

Automatic recognition of facial expressions is a challenging problem specially for low spatial resolution facial images. It has many potential applications in human–computer interactions, social robots, deceit detection, interactive video and behavior monitoring. In this study we present a novel framework that can recognize facial expressions very efficiently and with high accuracy even for very low resolution facial images. The proposed framework is memory and time efficient as it extracts texture features in a pyramidal fashion only from the perceptual salient regions of the face. We tested the framework on different databases, which includes Cohn–Kanade (CK+) posed facial expression database, spontaneous expressions of MMI facial expression database and FG-NET facial expressions and emotions database (FEED) and obtained very good results. Moreover, our proposed framework exceeds state-of-the-art methods for expression recognition on low resolution images.