- Show Keywords
- 3D Computer Vision 3D reconstruction Aerial Vision Augmented Reality Augmented Video Best Paper Award Biometrics Caleydo Computer Graphics Computer Vision Convex Optimization Coordinate transformations detection face Fingerprint Georeferencing GPU GUI HOG Human Computer Interaction Image Labelling Industrial Applications Information Visualization integral imaging Interaction Interaction Design Machine Learning Medical computer vision Medical Visualization Mixed Reality Mobile computing Mobile phone Model Multi-Display Environments Multiple Perspectives Object detection Object recognition Object reconstruction Object Tracking On-Line Learning Robotics Segmentation Shape analysis shape from focus SLAM Software Projects Structure from Motion Surveillance SVM Symmetry Tracking Fusion Tracking, Action Recognition User Interfaces Variational Methods Virtual reality and augmented reality Visual Tracking Visualization
Robust Fingerprint Recognition and Classification
The main aim of this project is to apply global models to fingerprint images for robust extraction of local features. These so-called minutiae features are used within classic pattern recognition algorithms for fingerprint matching (recognition, authentication). The direction field of a fingerprint is a crucial parameter for extracting minutiae features. Nevertheless many fingerprint images are of such poor quality, that the direction of the field can not be extracted for certain regions in the image. On the other hand it has been shown that if one can properly "guess" the direction, it is possible to apply enhancement algorithms which adaptively improve the clarity of ridges and furrows of such regions. In order to do this "guess work" computationally, a model for the directional field of a fingerprint must be applied during the extraction process. In another concept the extracted parameters of the directional field model can be employed for fingerprint classification.