Performance Comparison of Feature Extraction and Machine Learning Classification Algorithms for Face Recognition
DOI:
https://doi.org/10.30865/ijics.v5i3.3333Keywords:
PCA, LDA, Machine learning, Face recognition, Feature ExtractionAbstract
Face recognition is a highly active research topic in pattern recognition and computer vision, with numerous practical applications. Face recognition can provide the most natural interaction experience similar to the way humans can recognize others. This paper presents a performance comparison of various machine learning approaches and feature extraction algorithms. The feature extraction algorithm used is Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), and a combination of PCA-LDA. The method used is to take a dataset sample and then evaluate and compare machine learning algorithms to analyze accuracy in recognizing faces. We also use feature extraction techniques on facial image capture to speed up data processing. The classification algorithms measured are k-nearest neighbor, naive Bayes, support vector machine, random forest, and gradient boosting. The results showed that the random forest classification algorithm was the most accurate face recognition method. On the other hand, the PCA-LDA combined feature extraction algorithm has lower false-negative and false-positive rates than PCA and LDA. In addition, the PCA feature extraction algorithm has the fastest performance in the process of recognizing facesReferences
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