Fast and accurate holistic face recognition using Optimum-Path Forest

This paper presents a novel, fast and accurate holistic method for face-recognition using the Optimum-Path Forest (OPF) classifier. Our objective is to improve the face recognition accuracy against traditional methods and to reduce the computational effort in face recognition tasks. During the feature extraction stage we apply principal component analysis to reduce feature vectors in several dimensionalities. Experiments using face images from three public datasets (ORL, CBCL and YALE) present good results. Comparison among two other widely used supervised classifiers, artificial neural networks based on multilayer perceptron and support vector machines, show that the proposed method drastically reduces the computational cost, achieving correct classification rates at least identical to SVM.