Comparative study of statistical models and classifiers for face recognition
S. V. Pradeep, R. Srikantaswamy · 2017
A Biometric system is essentially a pattern-recognition system that recognizes a person based on feature vectors derived from a specific physiological or behavioural characteristics of a person. Biometrics represent the most secure way of identifying individuals. Because verification of identity is established using a physical and behavioural characteristics of a person and it is different for different individuals. In this work it is proposed to make a comparative study on various statistical models for feature extraction and different classifiers employed. Classical feature extraction techniques such as principal component analysis (PCA) also called eigenfaces Linear Discriminant Analysis (FLD) also called fisherfaces and classifiers including Euclidean distance classifier Mahalanobis distance classifier Radial basis function neural network (RBF) and ¡Support Vector Machine (SVM) are considered.