Bashir Hayat

Institute of Management Sciences Peshawar

Papers

Total Citations

H-Index

About

Bashir Hayat is a computer vision researcher whose work focuses on advancing human-computer interaction through facial expression recognition and deep learning. His key research areas include affective computing, intelligent visual surveillance, and human behavior analysis. Hayat’s most-cited paper, “Facial expression recognition using lightweight deep learning modeling” (2023, 18 citations), introduces an efficient deep learning approach for classifying seven universal emotions—happiness, sadness, anger, fear, contempt, surprise, and disgust. This work is notable for its practical applications in human-robot interaction and real-time surveillance systems, balancing accuracy with computational efficiency. By developing lightweight models, Hayat addresses the critical need for deployable AI in resource-constrained environments. His contributions help bridge the gap between complex deep learning architectures and real-world usability, making emotion recognition more accessible for interactive technologies. With growing recognition in the field, Hayat’s research continues to shape how machines perceive and respond to human emotional cues, promising safer, more intuitive human-machine collaboration.