Face Liveness Detection Method Based on Histogram of Oriented Gradient

Kong, Yueping; Liu, Xia; Xinqian, Xie; Li, Fengjie

doi:10.3788/lop55.031009

Cited by 2 publications

(1 citation statement)

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“…In view of these clues, some researchers have used Local Binary Patterns (LBP) to analyze image texture information and use Support Vector Machines (SVM) to classify living and non-living objects [2,3]. There are also three-dimensional orthogonal plane local binary patterns LBP-TOP [4], Histogram of Oriented Gradient (HOG) [5], and other features that analyze the texture difference of face images. However, the degree of texture features of the face anti-spoofing method is not effective for lowquality non-living face images.…”

Section: Introductionmentioning

confidence: 99%

Face Anti-Spoofing Method Based on Residual Network with Channel Attention Mechanism

Kong

Hao³

et al. 2022

Electronics

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The face recognition system is vulnerable to spoofing attacks by photos or videos of a valid user face. However, edge degradation and texture blurring occur when non-living face images are used to attack the face recognition system. With this in mind, a novel face anti-spoofing method combines the residual network and the channel attention mechanism. In our method, the residual network extracts the texture differences of features between face images. In contrast, the attention mechanism focuses on the differences of shadow and edge features located on nasal and cheek areas between living and non-living face images. It can assign weights to different filter features of the face image and enhance the ability of network extraction and expression of different key features in the nasal and cheek regions, improving detection accuracy. The experiments were performed on the public face anti-spoofing datasets of Replay-Attack and CASIA-FASD. We found the best value of the parameter r suitable for face anti-spoofing research is 16, and the accuracy of the method is 99.98% and 97.75%, respectively. Furthermore, to enhance the robustness of the method to illumination changes, the experiment was also performed on the datasets with light changes and achieved a good result.

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Section: Introductionmentioning

confidence: 99%