A team led by Zhiyi Yu of Sun Yat-sen University recently developed a new hand gesture recognition algorithm that strikes a balance between complexity, accuracy, and applicability. The team adopted innovative strategies to overcome key challenges and realize an algorithm that can be easily applied in consumer-level devices, as detailed in their paper published in the Journal of Electronic Imaging.
A main feature of the algorithm is adaptability to different hand types. "By first classifying the input gesture by hand type and then using sample libraries that match this type, we can improve the overall recognition rate with almost negligible resource consumption," Yu says.
Another key aspect is the use of a "shortcut feature" to perform a prerecognition step. "The gesture prerecognition step not only reduces the number of calculations and hardware resources required but also improves recognition speed without compromising accuracy," Yu says.
Tests showed that the approach could recognize hand gestures in real time with an accuracy exceeding 93%.
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