计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 429-433.doi: 10.11896/jsjkx.210300169
黄璞, 沈阳阳, 杜旭然, 杨章静
HUANG Pu, SHEN Yang-yang, DU Xu-ran, YANG Zhang-jing
摘要： 针对协同表示分类器(CRC)及其相关算法在处理人脸识别问题时面临的特征表达能力不强、鉴别能力弱等问题,提出了局部约束特征线表示分类器(LCFLRC)用于人脸识别。LCFLRC首先将待识别图像表示成其在所有特征线上的投影的线性组合,并根据待识别图像与特征线的距离对其施加约束,然后采用拉格朗日乘子法求解基于L2范数的最优化问题,最后,根据待识别图像与其在每类特征线上的投影的重构残差大小判断待识别图像的类别。LCFLRC采用待识别图像在特征线上的投影来表示待识别图像,能够获取有限人脸图像样本中的更多变化,同时利用了待识别图像与特征线的距离信息,使得离待识别图像较近的特征线上的投影在表示待识别图像时重构系数较大,因此包含更多的判别信息。在CMU PIE,Extended Yale-B以及AR人脸库上的实验结果表明,相比其他分类算法,所提算法在图像光照、人脸表情、姿态等变化方面的识别率有显著提升。
中图分类号:
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