Accurate fingertip detection is critical for translating hand gestures into actionable commands in vision-based human‒computer interaction(HCI)systems.However,challenges such as complex backgrounds,dynamic hand postur...Accurate fingertip detection is critical for translating hand gestures into actionable commands in vision-based human‒computer interaction(HCI)systems.However,challenges such as complex backgrounds,dynamic hand postures,and real-time processing constraints hinder reliable detection.This paper introduces a robust framework integrating three key innovations:(1)an adaptive Gaussian mixture model(GMM)enhanced with neighborhood pixel connectivity for precise motion extraction;(2)a weighted YCbCr color-space shadow removal algorithm to eliminate false positives;and(3)a centroid distance method refined with circularity constraints for accurate fingertip localization.Extensive experiments demonstrate a recognition accuracy of 97.26%across diverse scenarios,including varying illuminations,occlusions,and hand rotations.The algorithm processes each frame in 23.43 ms on average,satisfying real-time requirements.Comparative evaluations against state-of-the-art methods reveal significant improvements in precision(8.3%),recall(6.1%),and F-measure(7.8%).This work advances HCI applications such as virtual keyboards,gesture-controlled interfaces,and augmented reality systems.展开更多
基金funded by the Jiaying University Research Start-Up Fund(grant number 323E0431).
文摘Accurate fingertip detection is critical for translating hand gestures into actionable commands in vision-based human‒computer interaction(HCI)systems.However,challenges such as complex backgrounds,dynamic hand postures,and real-time processing constraints hinder reliable detection.This paper introduces a robust framework integrating three key innovations:(1)an adaptive Gaussian mixture model(GMM)enhanced with neighborhood pixel connectivity for precise motion extraction;(2)a weighted YCbCr color-space shadow removal algorithm to eliminate false positives;and(3)a centroid distance method refined with circularity constraints for accurate fingertip localization.Extensive experiments demonstrate a recognition accuracy of 97.26%across diverse scenarios,including varying illuminations,occlusions,and hand rotations.The algorithm processes each frame in 23.43 ms on average,satisfying real-time requirements.Comparative evaluations against state-of-the-art methods reveal significant improvements in precision(8.3%),recall(6.1%),and F-measure(7.8%).This work advances HCI applications such as virtual keyboards,gesture-controlled interfaces,and augmented reality systems.
