搜索资源列表
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PCA+LDA人脸识别，识别率高于单独PCA或LDA算法。需要matlab dimension reducation toolbox。-Face verification using PCA and LDA fusion. Better performance than single PCA or LDA algorithm. The image database is included. Matlab dimension reduction toolbox is requrired.
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对人脸图片用PCA降维，提取特征脸，附上ORL数据库-dimension reduction using PCA to extract eigenfaces of face images and the ORL dataset will also be provided
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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
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利用欧氏距离测试ar人脸数据库，该处的数据库经过了PCA降维处理；
训练样本为共计100个人的700幅图像降维数据
仅供参考，和交流之用。 - by using Euclidean distance test of AR face database, the database through the PCA dimension reduction
training sample reduction data for 700 images of 100 individua
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人脸搜索简单实现，使用主成分分析算法(PCA)，依赖opencv，对若干张人脸图片进行PCA降维处理，然后将输入人脸图片与降维后的数据做比较，根据权重输出结果，权重越大则人脸越相似-Simple face search, using the principal component analysis (PCA) algorithm, opencv, for a number of face images for PCA dimension reduction, and then the input
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