KEYWORDS: Independent component analysis, Neurons, Algorithm development, Neural networks, Performance modeling, Data modeling, Biometrics, Visual process modeling, Systems modeling, Digital signal processing
Among all existing biometric techniques, fingerprint-based identification is the oldest method, which has been
successfully used in numerous applications. Fingerprint-based identification is the most recognized tool in biometrics
because of its reliability and accuracy. Fingerprint identification is done by matching questioned and known friction skin
ridge impressions from fingers, palms, and toes to determine if the impressions are from the same finger (or palm, toe,
etc.). There are many fingerprint matching algorithms which automate and facilitate the job of fingerprint matching, but
for any of these algorithms matching can be difficult if the fingerprints are overlapped or mixed. In this paper, we have
proposed a new algorithm for separating overlapped or mixed fingerprints so that the performance of the matching
algorithms will improve when they are fed with these inputs. Independent Component Analysis (ICA) has been used as a
tool to separate the overlapped or mixed fingerprints.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.