A comparative analysis of feature extraction techniques for face recognition
Saket Karve, Vasisht Shende, Rizwan Ahmed · 2018
Face recognition finds extensive applications in various places. To build a fully functional and reliable system for recognizing faces, a robust and efficient face detection algorithm is required. Existing systems incorporate geometrical and template based approaches like distance between different points on the face, shape of the face, matching with existing templates and similar methods. These methods work successfully with specific images and have a tendency to fail, when an unusual image is tested. To overcome this, we propose statistical feature based approach in this study for face recognition. For this purpose, we make a detailed study of various feature extraction techniques involving principal and independent component analysis. Four different classifiers have been for the extracted features. Accuracy measurements and execution time have been recorded for the analysis. It is found that factor analysis method outperforms principal component analysis and independent component analysis with improved accuracy.