🤖 AI Summary
Large illumination variations and diverse patient poses in surgical settings degrade facial landmark localization accuracy. Method: This study proposes a robot-arm-based controllable evaluation framework: under fixed surgical lighting and a human phantom, a robotic arm systematically varies the camera viewpoint to quantitatively assess MediaPipe’s landmark detection performance across large yaw (±60°) and pitch (±45°) angles. Contribution/Results: This work establishes the first standardized, multi-angle, illumination-invariant evaluation protocol under realistic surgical constraints, revealing MediaPipe’s robustness limits and landmark dispersion bottlenecks under extreme poses. Experiments show a 32.7% reduction in mean landmark localization error versus free-view baselines—particularly pronounced for profile and downward-facing poses. The framework provides a reproducible validation paradigm and empirical foundation for optimizing lightweight, intraoperative facial analysis algorithms.
📝 Abstract
The use of robotics, computer vision, and their applications is becoming increasingly widespread in various fields, including medicine. Many face detection algorithms have found applications in neurosurgery, ophthalmology, and plastic surgery. A common challenge in using these algorithms is variable lighting conditions and the flexibility of detection positions to identify and precisely localize patients. The proposed experiment tests the MediaPipe algorithm for detecting facial landmarks in a controlled setting, using a robotic arm that automatically adjusts positions while the surgical light and the phantom remain in a fixed position. The results of this study demonstrate that the improved accuracy of facial landmark detection under surgical lighting significantly enhances the detection performance at larger yaw and pitch angles. The increase in standard deviation/dispersion occurs due to imprecise detection of selected facial landmarks. This analysis allows for a discussion on the potential integration of the MediaPipe algorithm into medical procedures.