Emotion recognition is defined as identifying human emotion and is directly related to
different fields such as human–computer interfaces, human emotional processing, irrational …

More and more real-world applications, especially in cognitive robotics, require the running
of different computer vision algorithms in parallel on board of embedded devices with limited …

Generally, facial expressions could be classified into two categories: static facial
expressions and micro-expressions. There are many promising applications of facial …

Existing facial expression recognition methods have some drawbacks. For example, it
becomes difficult for network learning on cross-dataset facial expressions, multi-region …

Using lightweight networks for facial expression recognition (FER) is becoming an important
research topic in recent years. The key to the success of FER with lightweight networks is to …

Facial Liveness Verification (FLV) is widely used for identity authentication in many security-
sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendors …

Face Verification Systems (FVSes) are more and more deployed by real-world mobile
applications (apps) to verify a human's claimed identity. One popular type of FVSes is called …

Transfer learning has become a promising field in machine learning owing to its wide
application prospects. Its effectiveness has spawned various methodologies and practices …

Purpose This research explored the intersection of cognitive processes, emotions and their
impacts on digital game-based vocabulary learning (DGVL) among university students …

Facial emotion recognition (FER) is rapidly advancing, with significant applications in
healthcare, human-computer interactions, and biometric security, driven by recent advances …