pca-face-recognition.zip_PCA-SVM_face recognition_matlab 图像处理_sv

# pca-face-recognition A simple face recognition demo using PCA algorithm. # Use KNN classifier Using the ORL face database. I set ```k = 90```, and the accuracy reach 92.5%. ```shell $ python pca_face_recognition.py s1/10.pgm is the most similar to s1/5.pgm s2/10.pgm is the most similar to s2/8.pgm s3/10.pgm is the most similar to s3/9.pgm s4/10.pgm is the most similar to s4/5.pgm s5/10.pgm is the most similar to s40/5.pgm s6/10.pgm is the most similar to s6/6.pgm s7/10.pgm is the most similar to s7/5.pgm s8/10.pgm is the most similar to s8/3.pgm s9/10.pgm is the most similar to s9/5.pgm s10/10.pgm is the most similar to s8/6.pgm s11/10.pgm is the most similar to s11/1.pgm s12/10.pgm is the most similar to s12/9.pgm s13/10.pgm is the most similar to s13/5.pgm s14/10.pgm is the most similar to s14/1.pgm s15/10.pgm is the most similar to s15/2.pgm s16/10.pgm is the most similar to s16/3.pgm s17/10.pgm is the most similar to s17/7.pgm s18/10.pgm is the most similar to s18/5.pgm s19/10.pgm is the most similar to s19/3.pgm s20/10.pgm is the most similar to s20/2.pgm s21/10.pgm is the most similar to s21/8.pgm s22/10.pgm is the most similar to s22/9.pgm s23/10.pgm is the most similar to s23/1.pgm s24/10.pgm is the most similar to s24/9.pgm s25/10.pgm is the most similar to s25/3.pgm s26/10.pgm is the most similar to s26/8.pgm s27/10.pgm is the most similar to s27/1.pgm s28/10.pgm is the most similar to s28/1.pgm s29/10.pgm is the most similar to s29/3.pgm s30/10.pgm is the most similar to s30/2.pgm s31/10.pgm is the most similar to s31/5.pgm s32/10.pgm is the most similar to s32/9.pgm s33/10.pgm is the most similar to s33/2.pgm s34/10.pgm is the most similar to s34/6.pgm s35/10.pgm is the most similar to s35/5.pgm s36/10.pgm is the most similar to s36/6.pgm s37/10.pgm is the most similar to s37/9.pgm s38/10.pgm is the most similar to s38/5.pgm s39/10.pgm is the most similar to s39/6.pgm s40/10.pgm is the most similar to s5/1.pgm accuracy: 0.925000 ``` # Use SVM classifier ```pca_svm_face_recogition.m``` is a Matlab code which implements a face recognition program using PCA to reduce the dimension of the features and one-vs-one multiclass SVM to classify the image. I used PCA to reduce the data to 50 dimensions and then use SVM linear kernel function to classify, finally, I got an accuracy of 0.9437. ``` accuracy = 0.9437 ``` Here is the eigen faces: