Experimental results demonstrate successful detection over a wide variety of facial variations in color, position, scale, rotation, pose, and expression from several photo collections. Modeling human faces provides a potential solution to the variations encountered on human face images. The modeling method adapts a generic face model to the given facial features, extracted from registered range and color images, in a global to local fashion. It iteratively moves the vertices of the mesh model to smoothen the non-feature areas, and uses the 2.5D active contours to refine feature boundaries. The resultant face model has been shown to be visually similar to the true face.
Initial results show that the constructed model is quite useful for recognizing profile views.
We describe a method for handwritten signature verification. The signatures are acquired using a digitizing tablet which captures both, dynamic and spatial information of the writing. After preprocessing the signature, several features are extracted. The similarity between an input signature and the reference set is computed using string matching and the similarity value is compared to a threshold. Experiments on a database containing 1,232 signatures of 102 individuals show that user-specific thresholds yield better results.