Abstract:
This paper proposes a fingerprint types
classification algorithm using Learning Vector
Quantization (LVQ) with FingerCode features. This
algorithm assigns each fingerprint image to one of
the five subclasses, according to the Henry system:
Arch(A), Tented Arch(T), Left Loop(L), Right
Loop(R), and Whorl Loop(W). The search for a
specific fingerprint can therefore be performed only
on specific subclasses containing a small portion of a
large database, which will save enormous
computational time. We use the feature vectors from
FingerCode generation process to train with the
LVQ classifiers. In our feature extraction process,
the oriented components are extracted from a
fingerprint image using a bank of Gabor filters, and
a feature vector is computed for each oriented
component. The feature vectors from the input image
are classified using LVQ classifier. This algorithm
has been tested the fingerprint database. For the 100
fingerprint images, the classification accuracy is 93
%, with 7 % error rate for 5-classes.
