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Living body recognition method, storage medium, and computer device

專利號
US11176393B2
公開日期
2021-11-16
申請人
TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED(CN Shenzhen)
發(fā)明人
Shuang Wu; Shouhong Ding; Yicong Liang; Yao Liu; Jilin Li
IPC分類
G06K9/62; G06K9/00; G06T7/194
技術(shù)領(lǐng)域
facial,image,liveness,confidence,model,training,live,target,feature,face
地域: Shenzhen

摘要

A face liveness recognition method includes: obtaining a target image containing a facial image; extracting facial feature data of the facial image in the target image; performing face liveness recognition according to the facial feature data to obtain a first confidence level using a first recognition model, the first confidence level denoting a first probability of recognizing a live face; extracting background feature data from an extended facial image, the extended facial image being obtained by extending a region that covers the facial image; performing face liveness recognition according to the background feature data to obtain a second confidence level using a second recognition model, the second confidence level denoting a second probability of recognizing a live face; and according to the first confidence level and the second confidence level, obtaining a recognition result indicating that the target image is a live facial image.

說明書

The confidence level is in one-to-one correspondence to the target image, and is used to indicate a confidence level of the target image being a live facial image. A live facial image is an image obtained by acquiring an image of a live face/person. A higher confidence level indicates a higher probability of the corresponding target image being a live facial image. In other words, a higher confidence level indicates a higher probability of the target image being an image obtained by acquiring an image of a live face/person. Understandably, the first confidence level here and the second confidence level to be mentioned later are both confidence levels, but correspond to confidence levels under different feature data conditions.

Specifically, the server may classify the target images according to the extracted facial feature data. When the extracted facial feature data matches the facial feature data of a live facial image, the target image is classed as a live facial image. When the extracted facial feature data matches the facial feature data of a non-live facial image, the target image is classed as a non-live facial image. The first confidence level indicates the degree of matching between the extracted facial feature data and the facial feature data of a live facial image. The higher the degree of matching between the extracted facial feature data and the facial feature data of a live facial image is, the higher the first confidence level will be, that is, the target image is more likely to be a live facial image.

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