计算机科学 ›› 2020, Vol. 47 ›› Issue (6): 121-125.doi: 10.11896/jsjkx.190500058
吴庆洪, 高晓东
WU Qing-hong, GAO Xiao-dong
摘要： 当前的人脸识别算法在理想环境下的识别正确率高,自适应能力强;但是在非理想环境下,人脸识别正确率急剧下降。为了提高人脸识别结果的稳定性,设计了稀疏表示和支持向量机相融合的非理想环境人脸识别算法。首先,提取非理想环境人脸的特征,并构建非理想环境人脸识别的特征字典;然后,采用特征字典对非理想环境人脸识别训练样本和测试样本进行处理,构建非理想环境人脸识别的学习样本;最后,采用支持向量机建立非理想环境人脸识别的分类器来对非理想环境人脸进行识别,并采用多个标准人脸数据库对所提非理想环境人脸识别算法进行测试。文中算法的非理想环境人脸识别正确率高,误识率和拒识率低,相对于其他人脸识别算法,其更适应环境的变化,对非理想环境人脸识别的整体效果更优,而且提高了非理想环境人脸识别的效率,具有十分明显的优越性。
中图分类号:
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