Comparison of Face Recognition Algorithms - Ensari, Tolga; GÜNAY, MELİKE (IEEE, 345 E 47Th St, New York, Ny 10017 USA, 2017)
in this study, we analyze the algorithms that is used for face recognition and make performance comparison of two algorithms. The methods that is analyzed are k-nearest neighbors, Naive Bayes, eigenfaces, principle component analysis (PCA) and k-means are implemented on ORL face dataset. As a result of the analysis, k-nearest neighbors algorithm and eigenfaces algoritm are the most successful and Naive Bayes has the worst performance result. Performance of k-nearest neighborhood which is the most succesfull one is decreasing from %94 to %91.5 after the princible component analysis. In addition, the difference increases to %7 for Naive Bayes algorithm.
Keywords: Machine Learning; Face Recognition; Principle Component Analysis (PCA); Naive Bayes; K-means; K-nearest Neighbor