哈尔滨商业大学学报（自然科学版）2025，Vol.41Issue(2)：176-183,8.
基于光照过滤的多模态活体检测
Multimodal live detection based on light filtering
摘要
Abstract
A multimodal face detection model,LFFAS,based on pseudo-negative sample generation combined with a light filtering module,was proposed to address the issues that face vivisection algorithms were susceptible to changes in light intensity and had low accuracy in cross-domain detection.RGB and LBP images were used as inputs to explore the feature information of vivisected faces.A light filtering module was constructed to eliminate the interference of lighting changes on the detection results.At the same time,the pseudo-negative sample generation method was employed to construct the hidden space of the living face,which helped defend against various unknown non-living body attacks.Experimental results showed that the half-total error rate(HTER)of the LFFAS model in the OCIM cross-domain test was 14.0%,15.19%,16.38%,and 14.01%,respectively,outperforming existing mainstream models.关键词
活体检测/数据融合/LBP算子/光照过滤/伪负样本生成/域泛化Key words
living detection/data fusion/LBP operator/light filtering/pseudo-negative sample generation/domain generalization分类
计算机与自动化引用本文复制引用
高文杰,邵叱风..基于光照过滤的多模态活体检测[J].哈尔滨商业大学学报（自然科学版）,2025,41(2):176-183,8.基金项目
国家自然科学基金(61572034) （61572034）
安徽高校与人工智能研究院协同创新项目(GXXT-2021-006). （GXXT-2021-006）
