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Chinese Researchers Develops New Hand Gesture Recognition Algorithm

A lot of effort has been devoted by researchers to unlock more natural forms of communication without requiring contact between the user and the device. Voice commands are a prominent example that has found their way into modern smartphones and virtual assistants, letting us interact and control devices through speech.

Hand gestures constitute another important mode of human communication that could be adopted for human-computer interactions. Recent progress in camera systems, image analysis and machine learning have made optical-based gesture recognition a more attractive option in most contexts than approaches relying on wearable sensors or data gloves.

To tackle these issues, a team led by Zhiyi Yu of Sun Yat-sen University, China, recently developed a new hand gesture recognition algorithm that strikes a good balance between complexity, accuracy, and applicability. As detailed in their paper, which was published in the Journal of Electronic Imaging, the team adopted innovative strategies to overcome key challenges and realise an algorithm that can be easily applied to consumer-level devices.

One of the main features of the algorithm is adaptability to different hand types. The algorithm first tries to classify the hand type of the user as either slim, normal, or broad-based on three measurements accounting for relationships between palm width, palm length, and finger length. If this classification is successful, subsequent steps in the hand gesture recognition process only compare the input gesture with stored samples of the same hand type.

Traditional simple algorithms tend to suffer from low recognition rates because they cannot cope with 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.

– Zhiyi Yu, Lead Author

Another key aspect of the team’s method is the use of a “shortcut feature” to perform a prerecognition step. While the recognition algorithm is capable of identifying an input gesture out of nine possible gestures, comparing all the features of the input gesture with those of the stored samples for all possible gestures would be very time-consuming. To solve this problem, the prerecognition step calculates a ratio of the area of the hand to select the three most likely gestures of the possible nine.

This simple feature is enough to narrow down the number of candidate gestures to three, out of which the final gesture is decided using a much more complex and high-precision feature extraction based on Hu invariant moments. The gesture prerecognition step not only reduces the number of calculations and hardware resources required but also improves recognition speed without compromising accuracy.

The team tested their algorithm both in a commercial PC processor and an FPGA platform using a USB camera. They had 40 volunteers make the nine hand gestures multiple times to build up the sample library, and another 40 volunteers to determine the accuracy of the system. Overall, the results showed that the proposed approach could recognise hand gestures in real-time with an accuracy exceeding 93%, even if the input gesture images were rotated, translated, or scaled. According to the researchers, future work will focus on improving the performance of the algorithm under poor lighting conditions and increasing the number of possible gestures. Gesture recognition has many promising fields of application and could pave the way to new ways of controlling electronic devices.

As reported by OpenGov Asia, China has made great achievements in scientific and technological innovation during the 13th Five-Year Plan period. As China embarks on a new journey to build a modern socialist country in all respects, sci-tech innovation will play a vital role in promoting the country’s overall development.

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