摘要
主成分分析(Principal Component Analysis,PCA)算法能够将高维问题简化成低维问题,具有简单、快速,且主成分之间相互正交,可消除原始数据成分间的影响,基于PCA算法的人脸识别技术能够在一定程度上去除光照、姿态、遮挡产生的噪音.使用核函数的支持向量机(Support Vector Machine,SVM)方法能够解决非线性问题,具备良好的分类效果.该算法结合PCA和SVM方法,先对未训练的图片进行PCA降维及特征提取,然后将特征输入到使用高斯核函数的SVM中进行训练.SVM分类器的性能采用10折交叉验证法进行验证.该方法较为适合工业园区无人巡逻车等对识别速度有较高要求的场景.
Principal Component Analysis (PCA) algorithm can simplify high-dimensional problems into low-dimensional problems. It is simple and fast, and the principal components are orthogonal to each other, which can eliminate the influence of the original data components. The face recognition technology based on PCA algorithm can remove noise caused by light, posture, and occlusion to some extent. The Support Vector Machine (SVM) method using kernel function can solve the nonlinear problem and has perfect classification effect. In this paper, combined with the PCA and SVM methods, dimension reduction and feature extraction are performed on the untrained images, and then the features are input into the SVM using the Gaussian kernel function for training. The performance of the SVM classifier is verified using 10-fold cross validation method. This method is suitable for scenes with high requirement for recognition speed, such as unmanned patrol car in industrial park.
作者
张庶
李子月
刘玉超
李琳
韩文
ZHANG Shu;LI Zi-Yue;LIU Yu-Chao;LI Lin;HAN Wen(National Engineering Laboratory for Integrated Command and Dispatch Technology, Beijing 100192, China;Navigation Research Center, Nanjing University of Aeronautics and Astronautics, Nanjing Jiangsu 210016, China;Chinese Institute of Command and Control, Beijing 100089, China;China Astronaut Research and Training Center, Beijing 100094, China;Information Engineering University, Zhengzhou Henan 450000, China)
出处
《指挥与控制学报》
2019年第3期249-253,共5页
Journal of Command and Control
