Harshita Jha

Papers

Total Citations

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About

Harshita Jha’s research lies at the intersection of computer vision, biometrics, and autonomous systems, with a particular focus on enhancing robotic surveillance through robust face recognition. Her most-cited work, “Effective Descriptors based Face Recognition Technique for Robotic Surveillance Systems” (2018), introduces the SPHORB descriptor—a fast and efficient method for real-time facial identification on mobile robots. By comparing SPHORB against conventional algorithms, Jha demonstrated significant improvements in recognition speed and accuracy, directly addressing the computational constraints of embedded surveillance platforms. This contribution is critical for deploying autonomous security bots that must reliably identify individuals in dynamic environments. With 5 citations, this paper has informed subsequent research in lightweight descriptor design for robotics. Jha’s work bridges the gap between theoretical descriptor engineering and practical deployment, offering a scalable solution for intelligent monitoring. Her research continues to shape how robots perceive and interact with human faces, making her a notable voice in the growing field of vision-based autonomous security.