计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 163-168.doi: 10.11896/jsjkx.190900118
邓良1, 许庚林1, 李梦杰1, 陈章进1,2
DENG Liang1, XU Geng-lin1, LI Meng-jie1, CHEN Zhang-jin1,2
摘要: 无论是使用传统的方法进行人脸识别,还是使用神经网络进行人脸识别,都存在运算量大、运算时间长等问题,很难对视频中的人脸进行实时检测与匹配。针对上述问题,使用轻量化神经网络进行人脸检测,使用运算简单的哈希算法计算人脸图像相似度,并对多个哈希相似度值加权进行人脸匹配,是减少运算时间、实现快速人脸识别的可行方案。使用轻量化神经网络Mobilenet作为人脸特征提取网络,使用剪枝的SSD模型作为检测网络,通过级联Mobilenet与SSD实现人脸的检测,之后对检测到的人脸图像进行识别。首先,分别计算人脸图像的均值哈希相似度与感知哈希相似度。然后,分别使用α和β作为均值哈希与感知哈希的加权系数对图像的均值哈希与感知哈希相似度值进行加权,并将结果作为图像的最终相似度。当加权后的相似度值大于设定的阈值I时,则认为两张图像中的人脸是同一个人;当加权后的相似度值小于设定的阈值K时,则认为两张图像中的人脸是不同的人。对于相似度处于阈值I和阈值K之间的图像,将它们按照相似度值从高到低的顺序择优匹配。所提方法在WiderFace和FDDB上的人脸检测准确率分别达到92.5%和94.2%,每张图片的平均处理时间为56ms;在ORL标准人脸库进行人脸匹配的准确率达到96.2%。使用摄像头进行实时人脸识别测试时,所提方法的人脸识别准确率为95%,平均人脸识别速度为80ms。实验证明,所提方法在保证较高准确率的前提下,能够实现实时的人脸检测与匹配。
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