Fingerprint classification system using fingerprint orientantion feature vectors obtained after passinf through different modules - Universidad Nacional de San Agustin (Arequipa - 2017).
- NIST4 database
- Preprocessing stages:
- Fingerprint Histogram Equalization
- Fingerprint Gabor Enhancement
- Fingerprint Threshold Binarization
- Fingerprint Thinning
- Final results:
- NIST4 4000 thinned fingerprint images
- Two features extracted:
- Region of Interes extraction through ANN detection algorithm
- Orientation Map 100 and 400 features extraction algorithm
- Final results:
- Manually extracted training database for roi block detection.
- NIST4 4000 roi fingerprint images of 200x200px
- 2 dat files containing: * 4000 NIST4 features vectors of 400 values from roi orientation map. * 4000 NIST4 features vectors of 100 values from roi orientation map.
- Fingerprint classification system:
- Fingerprint classification through Stacked Sparse Autoencoder using Keras.
- Fingerprint classification models with different parameters.
- Final results:
- Results from experiments of classification system
- Nice classification model of 88% accuracy found during experimentation available and ready to be loaded in keras.
- Spanish scratch Paper.