Papers
1
Total Citations
18
H-Index
1
About
Arshad Ahmad is a researcher advancing the field of computer vision, with a primary focus on facial expression recognition and human behavior analysis. His most-cited work, "Facial expression recognition using lightweight deep learning modeling" (2023, 18 citations), introduces an efficient deep learning approach to classify seven fundamental emotions—happiness, sadness, anger, fear, contempt, surprise, and disgust. This contribution is particularly impactful for applications in intelligent visual surveillance, human-robot interaction, and automated behavior analysis, where real-time, resource-efficient models are essential. By prioritizing lightweight architectures, Ahmad addresses the critical challenge of deploying sophisticated recognition systems on devices with limited computational power. His research bridges the gap between high-accuracy deep learning and practical, scalable deployment, making emotion-aware technology more accessible. Ahmad’s work continues to influence the development of responsive, human-centric AI systems, and his growing citation record reflects the relevance of his contributions to both academic research and real-world applications in affective computing and interactive robotics.
Research Focus
Key Achievements
Top Papers
- 1Facial expression recognition using lightweight deep learning modeling18 citations · 2023