A Comparative Study of Face Recognition Classification Algorithms

Due to the different classification effects and accuracy of different classification algorithms in machine learning, it is inconvenient for scientific researchers to choose which classification algorithm to use. This paper uses the face data published by Cambridge University as an experiment. The experiment first reduces the dimensionality of the data through the principal component analysis (PCA) algorithm, extracts the main features of the data, and then respectively through linear logic classification, linear discrimination LDA, nearest neighbor algorithm KNN, support vector machine SVM and the integrated algorithm Adaboost are used for classification. By comparing the advantages and disadvantages of the classification performance and complexity of different algorithms, the final review reviews accuracy, recall, f1-score, and AUC as evaluation indicators.