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Face Recognition Comparative Analysis Using Different Machine Learning Approaches

Ahmed Nisar, Khan Farhan Ajmal, Ullah Zain, Ahmed Hasnain, Shahzad Taimur, Ali Nableela

Advances in Science and Technology. Research Journal

2021

Vol. 15, no 1

265--272

The problem of a facial biometrics system was discussed in this research, in which different classifiers were used within the framework of face recognition. Different similarity measures exist to solve the performance of facial recognition problems. Here, four machine learning approaches were 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 was also seen and evaluated in terms of their ability to correctly classify a face. A combination of multiple algorithms such as PCA+1NN, LDA+1NN, PCA+ LDA+1NN, SVM, and SVM+PCA was used. All of them performed with exceptional values of above 90% but PCA+LDA+1N scored the highest average accuracy, i.e. 98%.