Facial recognition with PCA and machine learning methods

Facial recognition is a challenging problem in image processing and machine learning areas. Since widespread applications of facial recognition make it a valuable research topic, this work tries to develop some new facial recognition systems that have both high recognition accuracy and fast running speed. Efforts are made to design facial recognition systems by combining different algorithms. Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes. Also, PCA + KNN achieves the balance between recognition accuracy and running speed.