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zuijinlinfenlei
- 我们使用MATLAB软件实现了人脸识别并统计其识别率。本实验采用PCA（主成分分析）方法，利用K-L变换和奇异值分解原理实现。并分别采用最近邻法分类器得出它们的成功率。-We use face recognition software and the MATLAB Statistics recognition rate. The present study, PCA (principal component analysis) method, using KL transform and sin
PCA_SVM
- 采用经典的PCA对人脸图像进行特征提取，用SVM分类器进行分类。-Classic PCA face image feature extraction, classification with the SVM classifier.
pca
- pca算法实现人脸识别，包括数据图片，特征提取算法，最近邻分类器算法-pca algorithm for face recognition, including data pictures, feature extraction algorithm, nearest neighbor classifier algorithms, etc.
llde_cmb
- 人脸检测一直是人们在研究的问题，流形学习用于人脸检测中的特征提取，用PCA与constructM进行降维，KNN分类器用于分类。取得非常好的效果。-Face detection has been the problem of people in the study, manifold learning for face detection feature extraction using PCA and constructM dimension reduction, KNN classifier
matlab-face
- 基于PCA和欧几里得距离判据的模板匹配分类器-Euclidean distance criterion based on PCA and template matching classifier
matlab-face-recognition
- 基于PCA和FLD的人脸识别的线性分类器-Face recognition based on PCA and FLD linear classifier
人脸识别 MATLAB代码
- 使用pca方法对图像进行特征提取，对训练集的20个人的共一百张人脸进行训练，使用adaboost算法生成强分类器，可以对测试集的人脸图片进行识别，且识别率较高(The PCA method is used to extract the features of the image, and the training is carried out for a total of 100 faces of 20 people in the training set. The AdaBoost algor