Euclidean and geodesic distance between a facial feature points in two-dimensional face recognition system

Abstract EN

In this paper, we present two features extraction methods for two-dimensional face recognition.

We have used the facial feature point detection to compute the Euclidean Distance (ED) between all pairs of these points for the first approach of Face Feature Points (ED-FFP) and Geodesic Distance (GD-FFP) in the second one.

For a suitable comparison, we have employed three well-known classification techniques: Neural Networks (NN), k-Nearest Neighbor (KNN) and Support Vector Machines (SVM).

To test the present methods and evaluate its performance, a series of experiments were performed on two-dimensional face image databases (ORL and Yale).

Our results reveal that the extraction of image features is computationally more efficient using GD than ED.