Maksim Letenkov
Papers
2
Total Citations
6
H-Index
2
About
Maksim Letenkov is a researcher focused on the intersection of computer vision, deep learning, and human-robot interaction. His primary contributions lie in developing robust facial recognition systems for mobile robotic platforms operating in cyberphysical spaces. In his most cited work, "Fast Face Features Extraction Based on Deep Neural Networks for Mobile Robotic Platforms" (2020, 4 citations), he pioneered efficient neural network architectures that enable real-time face processing on resource-constrained robots. His earlier paper, "Method of synthetic data generation and architecture of face recognition system for interaction with robots in cyberphysical space" (2019, 2 citations), introduced an innovative approach to generating synthetic training samples, addressing the critical challenge of limited real-world facial data for robotic applications. This method allows for more reliable user identification in dynamic environments. Letenkov's work is particularly notable for bridging the gap between theoretical deep learning models and practical deployment on mobile platforms, making him a key contributor to the field of autonomous robotic perception. His research continues to influence how robots recognize and interact with humans in real-world settings.
Research Focus
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Top Papers
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