摘要
针对传统人脸识别算法运行效率低的问题,提出一种采用图像梯度补偿模式(IGC)的人脸快速识别算法.首先,提取人脸图像四个方位的梯度;其次,将所获的四个梯度进行多方式融合,产生两个梯度算子;再次,使用新产生的梯度算子对原图像进行适度补偿,形成人脸图像的IGC特征图;然后将所获IGC特征图分块统计直方图,并将各个分块的直方图串联成用于人脸图像描述的特征向量;最后使用PCA方式对特征向量进行降维处理,利用SVM分类器进行识别.在ORL和CMUPIE数据库上完成测试,结果表明本文算法在具有较高识别率的同时,其算法的运行效率具有卓越的表现.
To overcome the limitations of low efficiency of traditional face recognition,a novel method of face recognition based on Image Gradient Compensation pattern(IGC)is proposed.Firstly,gradient magnitude maps of a face image in four directions are calculated.Secondly,two gradient operators are produced by fusing the four gradients magnitude maps of a face image in multiple ways.Thirdly,the new gradient operators are used to compensate the original image and generate the IGC of the face image.Next,IGC feature maps are divided into several blocks,and the concatenated histogram calculated over all blocks is utilized as the feature descriptor of face recognition.Finally,Principal Component Analysis(PCA)is used to reduce the dimension of high-dimensional features.The recognition is performed by using the Support Vector Machine(SVM)classifier.Experimental results on YALE and CMU_PIE face databases validate that the algorithm in this study not only achieves high recognition rate,but also has excellent performance in computational efficiency.
作者
鄢丽娟
张彦虎
YAN Li-Juan;ZHANG Yan-Hu(School of Computer and Information Engineering,Guangdong Songshan Polytechnic,Shaoguan 512126,China)
出处
《计算机系统应用》
2020年第12期194-201,共8页
Computer Systems & Applications
基金
广东省普通高校特色创新项目(2019GKTSCX041)
广东省高职教育精品课程建设项目(粤教职函[2018]194.50)
韶关市科技计划(社会发展与农村科技专项)(2018SN041)
关键词
梯度
补偿
图像梯度补偿
人脸识别
主成分分析
支持向量机
gradient compensation
image gradient compensation
face recognition
Principal Component Analysis(PCA)
Support Vector Machine(SVM)
