Face recognition using weighted distance transform

Face recognition is one of the most important computer vision problems. Its importance is largely due to the security issues the world is facing at the moment and also the requirement of a more robust system security standard. This work investigates the use of facial weighted distance transform to improve the face recognition rate. Weighted distance transform, also known as geodesic distance, not only considers the spatial distance among pixels but also takes into account the local intensity variations providing a distance transform in the spatiointensity domain. Geodesic distance transform of facial images is estimated using the “Fast Marching” [1, 2] technique which is based on Dijkstra's algorithm employed to identify the shortest network path. It is a single pass algorithm providing efficient geodesic distance feature vector, thereby reducing the recognition time. A standard Frontal Face Data Base [3] is used to validate the algorithm. The obtained results are comparable to the state-of-the-art face recognition techniques.