Evaluation of facial landmark localization performance in a surgical setting
This work addresses the challenge of variable lighting and positioning for facial landmark detection in medical procedures like neurosurgery and plastic surgery, but it is incremental as it tests an existing algorithm in a new controlled environment.
The study evaluated the MediaPipe algorithm for facial landmark localization in a surgical setting, finding that improved accuracy under surgical lighting enhanced detection performance at larger yaw and pitch angles, though increased dispersion occurred due to imprecise landmark detection.
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.