Srikanth Sriram

Papers

Total Citations

H-Index

About

Srikanth Sriram is a researcher whose work sits at the intersection of embedded systems, computer vision, and real-time image processing. His primary research focus is on developing efficient, hardware-accelerated solutions for visual detection tasks, particularly leveraging system-on-chip (SoC) architectures. His most cited work, "Real Time Smile Detection using Haar Classifiers on SoC" (2014), demonstrates his core contribution: implementing complex computer vision algorithms—like Haar cascade classifiers—on low-power, embedded platforms such as the Raspberry Pi. By combining a CPU with a GPU-based architecture, Sriram showed that real-time facial expression analysis is feasible outside of high-end computing environments, opening doors for applications in human-computer interaction, robotics, and assistive technology. While his citation count (5) reflects a focused, early-career impact, this paper stands as a practical proof-of-concept for deploying machine learning models on resource-constrained devices. Sriram’s work is particularly valuable for students and researchers interested in bridging the gap between algorithmic vision research and real-world, low-cost deployment.