Yesi Novaria Kunang
Papers
1
Total Citations
1
H-Index
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About
Dr. Yesi Novaria Kunang is a researcher at the forefront of human-robot interaction and embedded artificial intelligence, with a primary focus on developing efficient, real-time computer vision systems for social robotics. Her most notable work addresses a critical challenge in robotics: enabling face recognition on low-power, resource-constrained devices. In her 2024 study, "The Memory Efficiency in a Receptionist Robot's Face Recognition System Using LBPH Algorithm," Dr. Kunang pioneered the use of the Local Binary Patterns Histogram (LBPH) algorithm on a Raspberry Pi platform. By implementing optimization techniques such as grayscale conversion and noise reduction, she demonstrated how to achieve reliable face recognition while dramatically reducing memory consumption—a breakthrough for deploying autonomous receptionist robots in real-world settings without expensive hardware. This work, already garnering citations, showcases her expertise in balancing algorithmic accuracy with computational efficiency. Dr. Kunang’s contributions are paving the way for more accessible, cost-effective social robots that can operate in offices, hospitals, and public spaces, making her a rising voice in the fields of embedded AI and assistive robotics.
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Top Papers
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