Fingerprint pattern classification
Pattern Recognition
Segmentation of fingerprint images using the directional image
Pattern Recognition
An approach to fingerprint filter design
Pattern Recognition
Detection of singular points in fingerprint images
Pattern Recognition
On the use of level curves in image analysis
CVGIP: Image Understanding
IEEE Spectrum
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous versus exclusive classification for fingerprint retrieval
Pattern Recognition Letters
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Classification by Directional Image Partitioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Gray-Scale Minutiae Detection In Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design and implementation of Log-Gabor filter in fingerprint image enhancement
Pattern Recognition Letters
Fingerprint orientation field estimation using ridge projection
Pattern Recognition
Fingerprint orientation field estimation using ridge projection
Pattern Recognition
Fingerprint ridge orientation estimation based on neural network
ISPRA'06 Proceedings of the 5th WSEAS International Conference on Signal Processing, Robotics and Automation
Fingerprint classification by SPCNN and combined LVQ networks
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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This paper presents a hierarchical neural network architecture for computing fingerprints block directional images. Two separately trained neural networks are connected in series. First. the fingerprint image is divided into 16 × 16 blocks, each block is submitted to the first network which is a back propagation neural network. It has four counters in its output layer one for each direction to count the main directional codes in each fingerprint block. The output of this network is considered the feature vector for the fingerprint block, which is then submitted to the second network. The second network is a self-organized feature maps neural network uses an unsupervised learning strategy to group the fingerprint blocks into distinct directional classes. In this scheme, there is more than one sub-class for each directional class, an agglomerative hierarchical cluster algorithm for merging two clusters is used to merge two classes if their corresponding distances are below a specified threshold. Results obtained with a real world data set indicate the effectiveness of the proposed architecture.