US 12,450,323 B2
Identity authentication method and system
Renyu Wang, Shenzhen (CN); Zhendong Gao, Shanghai (CN); Jing Wu, Shanghai (CN); Dong Wang, Beijing (CN); and Lantian Li, Beijing (CN)
Assigned to Huawei Technologies Co., Ltd., Shenzhen (CN); and Tsinghua University, Beijing (CN)
Filed by HUAWEI TECHNOLOGIES CO., LTD., Shenzhen (CN); and TSINGHUA UNIVERSITY, Beijing (CN)
Filed on May 25, 2023, as Appl. No. 18/323,971.
Application 18/323,971 is a continuation of application No. PCT/CN2021/101301, filed on Jun. 21, 2021.
Claims priority of application No. 202011341050 (CN), filed on Nov. 25, 2020.
Prior Publication US 2023/0297659 A1, Sep. 21, 2023
Int. Cl. G06F 21/32 (2013.01)
CPC G06F 21/32 (2013.01) 23 Claims
OG exemplary drawing
 
1. An identity authentication method, wherein the method is applied to an electronic device and comprises:
obtaining a biological feature extracted from a first registration object in a registration scenario;
obtaining a biological feature extracted from a first recognition object in a recognition scenario;
determining a likelihood score based on the biological feature of the first registration object and the biological feature of the first recognition object; and
determining, based on the likelihood score, whether an identity of the first recognition object is consistent with an identity of the first registration object, wherein
the likelihood score comprises a posterior probability part and a marginal probability part;
the posterior probability part is determined based on the biological feature of the first registration object, a probability distribution model of the registration scenario, and a mapping relationship between the probability distribution model of the registration scenario and a probability distribution model of the recognition scenario;
the probability distribution model of the registration scenario comprises probability distribution parameters between different registration objects and probability distribution parameters between different samples of a same registration object, based on a biological feature being extracted from one or more registration objects in the registration scenario,
the probability distribution model of the recognition scenario comprises probability distribution parameters between different recognition objects and probability distribution parameters between different samples of a same recognition object, based on a biological feature being extracted from one or more recognition objects in the recognition scenario; and
the marginal probability part is determined based on the biological feature of the first recognition object, the probability distribution model of the recognition scenario, and the mapping relationship.