On latent fingerprint minutiae extraction using stacked denoising sparse AutoEncoders

Latent fingerprint identification is of critical importance in criminal investigation. FBI's Next Generation Identification program demands latent fingerprint identification to be performed in lights-out mode, with very little or no human intervention. However, the performance of an automated l...

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Vydáno v:	IEEE International Joint Conference on Biometrics s. 1 - 7
Hlavní autoři:	Sankaran, Anush, Pandey, Prateekshit, Vatsa, Mayank, Singh, Richa
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 01.09.2014
Témata:	Feature extraction Manuals Neural networks NIST Noise measurement Noise reduction
On-line přístup:	Získat plný text
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Shrnutí:	Latent fingerprint identification is of critical importance in criminal investigation. FBI's Next Generation Identification program demands latent fingerprint identification to be performed in lights-out mode, with very little or no human intervention. However, the performance of an automated latent fingerprint identification is limited due to imprecise automated feature (minutiae) extraction, specifically due to noisy ridge pattern and presence of background noise. In this paper, we propose a novel descriptor based minutiae detection algorithm for latent fingerprints. Minutia and non-minutia descriptors are learnt from a large number of tenprint fingerprint patches using stacked denoising sparse autoencoders. Latent fingerprint minutiae extraction is then posed as a binary classification problem to classify patches as minutia or non-minutia patch. Experiments performed on the NIST SD-27 database shows promising results on latent fingerprint matching.
DOI:	10.1109/BTAS.2014.6996300