An image algorithm for computing the Hausdorff distance efficiently in linear time
Information Processing Letters
Computing the Hausdorff set distance in linear time for any Lp point distance
Information Processing Letters
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Algorithms
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human face recognition based on spatially weighted Hausdorff distance
Pattern Recognition Letters
An adaptive image Euclidean distance
Pattern Recognition
A cascade face recognition system using hybrid feature extraction
Digital Signal Processing
A memetic algorithm for efficient solution of 2D and 3D shape matching problems
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Robust estimation of distance between sets of points
Pattern Recognition Letters
Hierarchical kernel-based rotation and scale invariant similarity
Pattern Recognition
| Hi-index | 0.01 |
Face is considered to be one of the biometrics in automatic person identification. The non-intrusive nature of face recognition makes it an attractive choice. For face recognition system to be practical, it should be robust to variations in illumination, pose and expression as humans recognize faces irrespective of all these variations. In this paper, an attempt to address these issues is made using a new Hausdorff distance-based measure. The proposed measure represent the gray values of pixels in face images as vectors giving the neighborhood intensity distribution of the pixels. The transformation is expected to be less sensitive to illumination variations besides preserving the appearance of face embedded in the original gray image. While the existing Hausdorff distance-based measures are defined between the binary edge images of faces which contains primarily structural information, the proposed measure gives the dissimilarity between the appearance of faces. An efficient method to compute the proposed measure is presented. The performance of the method on bench mark face databases shows that it is robust to considerable variations in pose, expression and illumination. Comparison with some of the existing Hausdorff distance-based methods shows that the proposed method performs better in many cases.