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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Bibliographic Details
Published in:	IEEE International Joint Conference on Biometrics pp. 1 - 7
Main Authors:	Sankaran, Anush, Pandey, Prateekshit, Vatsa, Mayank, Singh, Richa
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.09.2014
Subjects:	Feature extraction Manuals Neural networks NIST Noise measurement Noise reduction
Online Access:	Get full text
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Summary:	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