Face Recognition Comparative Analysis Using Different Machine Learning Approaches

Abstract

The problem of a facial biometrics system is discussed in this research, in which different classifiers are used within the framework of face recognition. Different similarity measures are existed to solve the performance of facial recognition problems. Here four machine learning approaches are considered, namely, K-nearest neighbor (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Principal Component Analysis (PCA).The usefulness of multiple classification systems is also seen and evaluated in terms of their ability to correctly classify a face. We used combination of multiple algorithms such as PCA+1NN, LDA+1NN, PCA+ LDA+1NN, SVM, and SVM+PCA. All of them performed with exceptional values of above 90% but PCA+LDA+1N scored the highest average accuracy is 98%.

Keywords

• linear discriminant analysis
• k-nearest neighbor
• support vector machine
• principal component analysis