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Living body recognition method, storage medium, and computer device

專(zhuān)利號(hào)
US11176393B2
公開(kāi)日期
2021-11-16
申請(qǐng)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(lèi)
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(shuō)明書(shū)

With the above storage medium, after the target image is obtained, on the one hand, the facial feature data can be extracted automatically from the facial image in the target image, and then face liveness recognition is performed based on the facial feature data so that a probability of recognizing a face liveness is obtained; on the other hand, the background feature data can be extracted automatically from the extended facial image in the target image, and then face liveness recognition is performed based on the background feature data so that a probability of recognizing a face liveness is obtained. In this way, with reference to the two probabilities, a recognition result is obtained indicating whether the target image is a live facial image. This not only ensures accuracy of face liveness detection to some extent, but also avoids time consumption caused by necessity of user cooperation and interaction, thereby improving efficiency of the face liveness detection.

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