3. 特征提取:利用MATLAB的人脸识别工具包,如Face Recognition Toolbox,对处理后的图像提取人脸特征,常用的方法包括主成分分析(PCA)和线性判别分析(LDA)等。 4. 训练模型:使用已提取的人脸特征数据集训练人脸识别模型,可以选择支持向量机(SVM)、卷积神经网络(CNN)等算法。 5. 考勤系统:在员工打卡时,将摄像头捕获的...
wingrid.Analyze.plot_comps : plot 2 components of PCA- or LDA-transformed features in a scatterplot, where data can be colored and labeled by a variable labels and filtered by a Boolean mask, filter_by. wingrid.Analyze.loadings_plot_bar : plot the loadings of a component (the contribution...
Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused....
aThe most widely used feature extraction methods are Principal Components Analysis (PCA)and Linear Discriminant Analysis (LDA), which are both linear projection methods, unsupervised and supervised respectively. 最用途广泛的特征抽出方法是主要成分分析 (PCA)和线性有识别力的分析 (LDA),分别为两个线性投射...