S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

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RGB-D Depth-Sensor-Based Hand Gesture Recognition Using Deep Learning of Depth Images with Shadow Effect Removal for Smart Gesture Communication

Ing-Jr Ding and Nai-Wei Zheng

(Received May 26, 2021; Accepted November 25, 2021)

Keywords: RGB-D depth sensor, shadow effect, serial binary image extraction, deep learning, hand gesture recognition

Recently, compound image sensor devices have been widely used to construct many next-generation human–machine interaction applications, including hand gesture action recognition. Such devices, generally known as RGB-D devices, contain an RGB color camera and a depth sensor set. The depth sensor in RGB-D devices is essentially a set of a specific type of sensor and comprises one IR projector and one IR camera. This structure of the depth sensor inevitably generates an undesired shadow effect, adversely affecting hand gesture recognition. To tackle this issue and alleviate the shadow effect on hand gesture recognition, we have developed a serial binary image extraction approach. The proposed approach is essentially composed of two consecutive computation phases, phase-1 and phase-2 binary image extraction. In this work, the Kinect compound sensor device is employed to capture hand gesture depth images. The deep learning model, a visual geometry group (VGG)-type convolutional neural network (CNN), i.e., the well-known VGG-CNN, is utilized to evaluate the recognition effectiveness of improved hand gesture depth images derived from serial binary image extraction. Ten hand gestures that are common in daily life are chosen to evaluate depth-sensor-based interactive action recognition. Experimental results show that the proposed serial binary image extraction can effectively eliminate the undesired shadow region in hand gesture depth images and significantly improve the recognition accuracy of VGG-type CNN hand gesture recognition. The proposed depth-sensor-based hand gesture recognition approach can benefit people requiring interaction action recognition and further promote smart gesture communication.

Corresponding author: Ing-Jr Ding




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