TY - GEN
T1 - WiLCA
T2 - 19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2022
AU - Lin, Chi
AU - Ji, Chuanying
AU - Ma, Fenglong
AU - Wang, Lei
AU - Zhong, Wei
AU - Wu, Guowei
N1 - Funding Information:
ACKNOWLEDGEMENTS This research is sponsored in part by the National Natural Science Foundation of China under Grant 62172069, 61872052, 62027826, 61872053, 61872178, 62072067, U1908214, the Xinghai Scholar Program in Dalian University of Technology, Natural Science Foundation of Liaoning Province under Grant No.2019-MS-055, Youth Science and Technology Star of Dalian under Grant 2018RQ45, Fundamental Research Funds for the Central Universities under Grant No. DUT21JC27, DUT20RC(3)039, the CCF-Tencent Open Fund under Grant IAGR20210116.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Human authentication is critical to protect personal and property security. Existing contactless authentication methods face some drawbacks, such as requiring large data size and low accuracy in cross-domain recognition, which hinders widespread popularization in practical applications. In this paper, we design and implement WiLCA, a WiFi-based lightweight contactless authentication system. First, we devise a Channel State Information (CSI) stream selection scheme to extract human movement features and reduce the sample size in the recognition process. Then, an AGO model is proposed, in which a Siamese Neural Network (SNN) framework with a cross-entropy module is used to guarantee accurate human authentication with limited data, and a lightweight GhostNet accelerates authentication with cheap operations. At last, extensive experiments are conducted to demonstrate the advantages of WiLCA, revealing that compared with state-of-the-art methods, WiLCA can reduce the data size by at least 2.5 x and achieve accurate authentication with an accuracy of over 98%.
AB - Human authentication is critical to protect personal and property security. Existing contactless authentication methods face some drawbacks, such as requiring large data size and low accuracy in cross-domain recognition, which hinders widespread popularization in practical applications. In this paper, we design and implement WiLCA, a WiFi-based lightweight contactless authentication system. First, we devise a Channel State Information (CSI) stream selection scheme to extract human movement features and reduce the sample size in the recognition process. Then, an AGO model is proposed, in which a Siamese Neural Network (SNN) framework with a cross-entropy module is used to guarantee accurate human authentication with limited data, and a lightweight GhostNet accelerates authentication with cheap operations. At last, extensive experiments are conducted to demonstrate the advantages of WiLCA, revealing that compared with state-of-the-art methods, WiLCA can reduce the data size by at least 2.5 x and achieve accurate authentication with an accuracy of over 98%.
UR - http://www.scopus.com/inward/record.url?scp=85141196168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85141196168&partnerID=8YFLogxK
U2 - 10.1109/SECON55815.2022.9918594
DO - 10.1109/SECON55815.2022.9918594
M3 - Conference contribution
AN - SCOPUS:85141196168
T3 - Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops
SP - 316
EP - 324
BT - 2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2022
PB - IEEE Computer Society
Y2 - 20 September 2022 through 23 September 2022
ER -