Mayank Raj

Papers

Total Citations

H-Index

About

Mayank Raj is a researcher focused on the intersection of computer vision, biometrics, and robotics, with a particular emphasis on enhancing security and surveillance systems. His most cited work, "Surveillance Robots based on Pose Invariant Face Recognition Using SSIM and Spectral Clustering" (2018), addresses a critical challenge in real-world face recognition: handling variations in pose. By integrating Structural Similarity Index (SSIM) with spectral clustering, Raj proposed a method that improves the robustness of face recognition on mobile surveillance platforms, enabling more reliable identification even under non-ideal conditions. This contribution is pivotal for advancing autonomous security robots, where accurate, pose-invariant recognition is essential. While his citation count is currently modest, his work represents a practical step toward deploying biometric systems in dynamic environments, bridging the gap between laboratory algorithms and field-ready applications. Raj’s research underscores the growing importance of combining machine learning techniques with robotic systems to solve pressing cybersecurity and authentication problems, making his work relevant for students and researchers exploring the future of intelligent surveillance and biometric security.