PCA vs low resolution images in face verification

Abstract

Principal components analysis (PCA) has been one of the most applied methods for face verification using only 2D information, in fact, PCA is practically the method of choice for face verification applications in the real-world. An alternative method to reduce the problem dimension is working with low resolution images. In our experiments, three classifiers have been considered to compare the results achieved using PCA versus the results obtained using low resolution images. An initial set of located faces has been used for PCA matrix computation and for training all classifiers. The images belonging to the testing set were chosen to be different from the training ones. Classifiers considered are k-nearest neighbours (KNN), radial basis function (RBF) artificial neural networks, and support vector machine (SVM). Results show that SVM always achieves better results than the other classifiers. With SVM, correct verification difference between PCA and low resolution processing is only 0.13% (99.52% against 99.39%).