Euclidean & Geodesic Distance between a Facial Feature Points in Two-Dimensional Face Recognition System

In this paper, we present two feature extraction methods for two-dimensional face recognition. Our approaches are based on facial feature points detection then compute the Euclidean Distance between all pairs of this points for a first method (ED-FFP) and Geodesic Distance in the second approach (GD-FFP). These measures are employed as inputs to a commonly used classification techniques such as Neural Networks (NN), kNearest 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). The recognition rate across all trials was higher using Geodesic Distance (GD-FFP) than Euclidean Distance (ED-FFP). The experimental results also indicated that the extraction of image features is computationally more efficient using Geodesic Distance than Euclidean Distance. Keywords—face recognition, Euclidean Distance, Geodesic Distance, Neural Networks, k-Nearest Neighbor, Support Vector Machines.