Feature Extraction & Minutiae Matching
The first step for this verification approach is normalization, which results in a better contrast of the fingerprint input. After that, the fingerprint is segmented, which crops areas of the recorded input, which do not contain any relevant information. After that, the fingerprint is filtered using a Gabor filter. Now, it is possible to create the feature map, which is used as the template. This template is matched in the subsequent matching step with templates of other fingerprints. The result of the matching is the matching score, which represents how good two fingerprints resemble each other.
The template matching is done by using a classifier which is based on the k-nearest neighborhood algorithm KNN. “Training” of the KNN consists simply of collecting k images per individual as the training set. The remaining images consists of the testing set. The classifier finds the k points in the training set that are the closest to x (relative to the Euclidean distance) and assigns x the label shared by the majority of these k nearest neighbors.
The last phase is the verification phase where the testing fingerprint image
The output of the CNN is inputted to the system
Magnitude features are extracted
The KNN algorithm is performed
Identification of the person