Face recognition based on geodesic distance approximations between multivariate normal distributions

We propose a novel generative approach for face recognition, in which sparse facial features are extracted from high resolution color face images using predefined landmark topologies which mark discriminative locations on face images, unlike the appearance-based approach, in which low resolution grayscale face images are used, reducing the computational complexity. By adopting a common landmark topology, the dissimilarity between distinct face images can be scored in terms of the dissimilarities between their corresponding landmarks, which are obtained by proposed geodesic distance approximations between multivariate normal distributions which represent the color intensities in the vicinities of each landmark location. The classification process of new face samples occurs by the determination of the face image sample present in the training set which minimizes the dissimilarity score. The proposed method was compared with representative current state-of-the-art methods using color or grayscale face images and presented the higher recognition rates. Moreover, these results also support a trend in which color information is relevant in face recognition.