计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 443-447.doi: 10.11896/jsjkx.200900207
卓雅倩, 欧博
ZHUO Ya-qian, OU Bo
摘要: 智能时代,人脸识别算法是智能身份认证的关键支撑技术之一,在门禁、手机解锁和金融支付领域有着重要应用。而人脸防伪识别则是用来增强其识别安全性,对抗伪造人脸攻击和鉴别真实人脸的防御性技术,相关研究颇多。其中基于LBP(local binary pattern)的人脸防伪算法综合性能较好,但是现有算法在噪声场景下的识别性能还难以令人满意。为此,文章提出基于相邻像素对的PLBP(pairwise local binary pattern)特征模式,通过充分挖掘像素对之间的相关性,来改进噪声环境下的算法性能。相比于LBP,所提算法以相邻像素对均值为基准与邻域其余像素比较生成二进制模式,从而能够利用像素对间的空间相关性来获取新的人脸特征。实验结果表明,该算法与主流LBP算法相比性能有所提升。其在无噪声条件下准确率接近了95.05%,在有高斯噪声环境下则能有效降低性能损失。相比其他算法在高斯噪声环境下的准确率下降情况,所提算法表现稳定,有着较好的鲁棒性。
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