Geodesic distances are a natural dissimilarity measure between probability distributions of a fixed type, and are used to discriminate texture in several image-based measurements. Furthermore, since there is no known closed-form solution for the geodesic distance between general multivariate normal distributions, we propose two efficient approximations to be used as texture dissimilarity metrics in the context of face recognition. Moreover, we propose a novel approach for face recognition based on texture discrimination unlike the typical appearance-based approach that uses low-resolution grayscale face images. In our face recognition approach, sparse facial features are extracted from high-resolution color face images using predefined landmark topologies which mark discriminative locations on the face images. By adopting a common landmark topology, the dissimilarity between distinct face images can be scored in terms of the dissimilarities (obtained by the proposed texture dissimilarity metrics) between their corresponding landmarks represented by multivariate normal distributions which express the color distribution in the vicinities of each landmark location. The classification of new face image samples occurs by determining the face image sample in the training set which minimizes the dissimilarity score in the nearest neighbor classification approach. This new face recognition method was compared to methods representative of the state-of-the-art, using color or grayscale face images, and presented higher recognition rates. Moreover, the proposed texture dissimilarity metric applied in face recognition is also efficient in general texture discrimination (e.g., texture recognition of material images), as our experiments suggest.
|Number of pages||14|
|Journal||Measurement, Science and Technology|
|Early online date||31 Aug 2018|
|Publication status||Published - 3 Oct 2018|
- geodesic distance, approximations, multivariate normal distribution, face recognition, sparse features.