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Simple, correct, and environment friendly: Improving the way computers recognize hand gestures

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Images of the 9 interactive hand gestures in the research. Credit: Zhang et al., doi: 10.1117/1.JEI.30.6.063026.

In the 2002 science fiction blockbuster movie “Minority Report,” Tom Cruise’s character John Anderton makes use of his palms, sheathed in particular gloves, to interface together with his wall-sized clear laptop display screen. The laptop acknowledges his gestures to enlarge, zoom in, and swipe away. Although this futuristic imaginative and prescient for computer-human interplay is now 20 years previous, at the moment’s people nonetheless interface with computers through the use of a mouse, keyboard, distant management, or small contact display screen. However, a lot effort has been devoted by researchers to unlock extra pure types of communication with out requiring contact between the consumer and the system. Voice instructions are a outstanding instance which have discovered their way into fashionable smartphones and digital assistants, letting us work together and management gadgets by speech.

Hand gestures represent one other vital mode of human communication that may very well be adopted for human-computer interactions. Recent progress in digicam techniques, picture evaluation and machine studying have made optical-based gesture recognition a extra engaging possibility in most contexts than approaches counting on wearable sensors or knowledge gloves, as utilized by Anderton in “Minority Report.” However, present strategies are hindered by a wide range of limitations, together with excessive computational complexity, low velocity, poor accuracy, or a low variety of recognizable gestures. To sort out these points, a staff led by Zhiyi Yu of Sun Yat-sen University, China, just lately developed a brand new hand gesture recognition algorithm that strikes steadiness between complexity, accuracy, and applicability. As detailed of their paper, which was revealed in the Journal of Electronic Imaging, the staff adopted progressive methods to beat key challenges and understand an algorithm that may be simply utilized in consumer-level gadgets.

One of the major options of the algorithm is adaptability to totally different hand sorts. The algorithm first tries to categorise the hand sort of the consumer as both slim, regular, or broad based mostly on three measurements accounting for relationships between palm width, palm size, and finger size. If this classification is profitable, subsequent steps in the hand course of solely evaluate the enter gesture with saved samples of the identical hand sort. “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,” explains Yu.

Another key facet of the staff’s methodology is the use of a “shortcut feature” to carry out a prerecognition step. While the recognition algorithm is able to figuring out an enter gesture out of 9 attainable gestures, evaluating all the options of the enter gesture with these of the saved samples for all attainable gestures could be very time consuming. To remedy this drawback, the prerecognition step calculates a ratio of the space of the hand to pick out the three almost definitely gestures of the attainable 9. This easy function is sufficient to slender down the variety of candidate gestures to 3, out of which the last gesture is determined utilizing a way more advanced and high-precision function extraction based mostly on “Hu invariant moments.” Yu says, “The gesture prerecognition step not only reduces the number of calculations and hardware resources required but also improves recognition speed without compromising accuracy.”

The staff examined their algorithm each in a industrial PC processor and an FPGA platform utilizing an USB digicam. They had 40 volunteers make the 9 hand gestures a number of occasions to construct up the pattern library, and one other 40 volunteers to find out the accuracy of the system. Overall, the outcomes confirmed that the proposed strategy may recognize in actual time with an accuracy exceeding 93%, even when the enter pictures have been rotated, translated, or scaled. According to the researchers, future work will deal with bettering the efficiency of the algorithm below poor lightning situations and growing the variety of attainable gestures.

Gesture recognition has many promising fields of software and may pave the way to new methods of controlling digital gadgets. A revolution in human-computer interplay may be shut at hand!


WaveGlove: A glove with 5 inertial sensors for hand gesture recognition


More info:
Qiang Zhang et al, Hand gesture recognition algorithm combining hand-type adaptive algorithm and effective-area ratio for environment friendly edge computing, Journal of Electronic Imaging (2021). DOI: 10.1117/1.JEI.30.6.063026

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Simple, correct, and environment friendly: Improving the way computers recognize hand gestures (2021, December 28)
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