Saadia Malik

Papers

Total Citations

H-Index

About

Saadia Malik is a computer vision researcher whose work focuses on advancing human-computer interaction through gesture-based recognition systems. Her key research areas include hand pose estimation, deep learning architectures, and real-time gesture analysis. Her most cited work, "Hand Pose Recognition Using Parallel Multi Stream CNN" (2021, 11 citations), introduces a novel parallel multi-stream convolutional neural network that significantly improves the accuracy of hand pose recognition in dynamic environments. This contribution addresses a critical challenge in enabling natural, touchless interfaces for applications ranging from sign language interpretation to robot control and smart surveillance. By designing a system that processes multiple feature streams simultaneously, Malik's approach enhances robustness against variations in lighting, background, and hand orientation. Her research has practical implications for assistive technologies and immersive user experiences. As a rising voice in applied machine learning, Malik's work bridges the gap between theoretical deep learning advances and real-world deployment, demonstrating how parallel CNN architectures can make gesture-based interfaces more reliable and responsive for everyday use.