Improved facial feature sets for enhancing face recognition system - Bai, S.PrincySuganthi; Latha, Dr. D. PonmaryPushpa (International Journal of Engineering and Technology, 2026)
Face recognition is the vogue technology in the field of biometrics for providing surveillance facility, which is most needed in public and domestic places. With the upcoming robust algorithms for face recognition, following are the challenges noted and that have a major impact in facial recognition system such as illumination variation, pose, expression, weight variation, plastic surgery etc… that must be redefined by using long-lasting feature set and efficient classifier that suits well for the feature set. In this paper, four efficient feature sets are formed by incorporating novel ideas on the fundamental traditional feature extracting methodologies such as KPCA (Kernel Principal Components), FFT (Fast Fourier Transform) and texture features which are collected from ORL (Olivetti Research Laboratory-AT&T) data base using MATLAB2015. The feature sets are analyzed with the classifiers like SVM (Support Vector Machine) and KNN (K-Nearest Neighbour) using IBM SPSS Predictive Modeler. The feature sets are fine tuned for its accuracy with three different kernels of diverse gamma values during SVM classification process. In order to fix the best K neighbours during KNN classification on the four feature sets and SVPC feature, cross validation techniques with five and ten folds are done in order to attain the best k value, which produces better accuracy.