Hand gesture recognition has long been a study topic in the field of Human Computer Interaction. Compared with traditional camera-based recognition systems, radar can realize dynamic hand gesture recognition at long distances and in low light conditions by exploiting the micro-Doppler (m-D) effect. However, for some static and complex hand gestures, the m-D-based recognition methods are rendered impotent. In this paper, we present a method based on 2-D synthetic aperture radar (SAR) imaging to distinguish nine kinds of static hand gestures representing the numbers 1-9. A cost-effective 77 GHz mm-wave radar is used to achieve imaging and data acquisition of different gestures. Finally, two classifiers including classic machine learning and deep learning are used to evaluate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method can guarantee a promising recognition accuracy.
Phase extraction from a single closed interferogram with a quadratic phase plays an important role in optical interferometry. Based on the energy of the interference images of this type is concentrated in a very narrow range around a point in the fractional Fourier transform (FRFT) domain under matched angle, the FRFT technique is a useful parameter estimator for the fringe pattern, but it has not been used to retrieve the desired phase from the fringe pattern. Thus, the parameter estimation approach based on FRFT is extended to the extract phase. The phase extraction can be done without using a phase unwrapping algorithm. Moreover, for ensuring both the estimation precision and speed, a coarse-to-fine searching strategy that includes a direct searching process implemented with a large step size and an iterative searching based on quasi-Newton method is presented in this paper to implement the FRFT method. The feasibility and applicability of the proposed approach are demonstrated using simulation and experimental results. The experimental results show that the proposed method is robust to noise and obstacles.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.