Simple, accurate, and efficient: Improving the way computers recognize
hand gestures
Optical hand gesture recognition sees improvements in accuracy and
complexity with new algorithm

Date:
December 27, 2021
Source:
SPIE--International Society for Optics and Photonics
Summary:
Recent progress in camera systems, image analysis, and machine
learning have made optical-based gesture recognition a more
attractive option in most contexts. However, current methods are
hindered by a variety of limitations, including high computational
complexity, low speed, poor accuracy, or a low number of
recognizable gestures.



FULL STORY ==========================================================================
In the 2002 science fiction blockbuster film Minority Report, Tom Cruise's character John Anderton uses his hands, sheathed in special gloves, to interface with his wall-sized transparent computer screen. The computer recognizes his gestures to enlarge, zoom in, and swipe away. Although
this futuristic vision for computer-human interaction is now 20 years
old, today's humans still interface with computers by using a mouse,
keyboard, remote control, or small touch screen. However, much 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 have 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,
as used by Anderton in Minority Report.

However, current methods are hindered by a variety of limitations,
including high computational complexity, low speed, poor accuracy, or
a low number of recognizable gestures. 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 realize an algorithm that can be easily applied in 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,"
explains Yu.

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." 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 team tested their algorithm both in a commercial PC processor and an
FPGA platform using an 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 recognize 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 lightning 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. A
revolution in human- computer interaction might be close at hand! ========================================================================== Story Source: Materials provided by SPIE--International_Society_for_Optics_and_Photonics.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Qiang Zhang, Shanlin Xiao, Zhiyi Yu, Huanliang Zheng, Peng
Wang. Hand
gesture recognition algorithm combining hand-type adaptive algorithm
and effective-area ratio for efficient edge computing. Journal of
Electronic Imaging, 2021; 30 (06) DOI: 10.1117/1.JEI.30.6.063026 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/12/211227154426.htm

--- up 3 weeks, 2 days, 7 hours, 13 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)