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The uniqueness, firmness, public recognition, and its minimum risk of
intrusion made fingerprint is an expansively used personal authentication
metrics. Fingerprint technology is a biometric technique used to distinguish
persons based on their physical traits. Fingerprint based authentication
schemes are becoming increasingly common and usage of these in
fingerprint security schemes, made an objective to the attackers. The repute
of the fingerprint image controls the sturdiness of a fingerprint authentication
system. We intend for an effective method for fingerprint classification with
the help of soft computing methods. The proposed classification scheme is
classified into three phases. The first phase is preprocessing in which the
fingerprint images are enhanced by employing median filters. After noise
removal histogram equalization is achieved for augmenting the images. The
second stage is the feature Extraction phase in which numerous image
features such as Area, SURF, holo entropy, and SIFT features are extracted.
The final phase is classification using hybrid Neural for classification of
fingerprint as fake or original. The neural network is unified with BAT
algorithm for optimizing the weight factor
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