152 research outputs found
UBSegNet: Unified Biometric Region of Interest Segmentation Network
Digital human identity management, can now be seen as a social necessity, as
it is essentially required in almost every public sector such as, financial
inclusions, security, banking, social networking e.t.c. Hence, in today's
rampantly emerging world with so many adversarial entities, relying on a single
biometric trait is being too optimistic. In this paper, we have proposed a
novel end-to-end, Unified Biometric ROI Segmentation Network (UBSegNet), for
extracting region of interest from five different biometric traits viz. face,
iris, palm, knuckle and 4-slap fingerprint. The architecture of the proposed
UBSegNet consists of two stages: (i) Trait classification and (ii) Trait
localization. For these stages, we have used a state of the art region based
convolutional neural network (RCNN), comprising of three major parts namely
convolutional layers, region proposal network (RPN) along with classification
and regression heads. The model has been evaluated over various huge publicly
available biometric databases. To the best of our knowledge this is the first
unified architecture proposed, segmenting multiple biometric traits. It has
been tested over around 5000 * 5 = 25,000 images (5000 images per trait) and
produces very good results. Our work on unified biometric segmentation, opens
up the vast opportunities in the field of multiple biometric traits based
authentication systems.Comment: 4th Asian Conference on Pattern Recognition (ACPR 2017
Segmentation of slap fingerprints
This thesis describes a novel algorithm that segments the individual fingerprints in a multi-print image. The algorithm identifies the distal phalanx portion of each finger that appears in the image and labels them as an index, middle, little or ring finger. The accuracy of this algorithm is compared with the publicly-available reference implementation, NFSEG, part of the NIST Biometric Image Software (NBIS) suite developed at National Institute of Standards and Technology (NIST). The comparison is performed over large set of fingerprint images captured from unique individuals
Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge
We propose a fully automatic minutiae extractor, called MinutiaeNet, based on
deep neural networks with compact feature representation for fast comparison of
minutiae sets. Specifically, first a network, called CoarseNet, estimates the
minutiae score map and minutiae orientation based on convolutional neural
network and fingerprint domain knowledge (enhanced image, orientation field,
and segmentation map). Subsequently, another network, called FineNet, refines
the candidate minutiae locations based on score map. We demonstrate the
effectiveness of using the fingerprint domain knowledge together with the deep
networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004)
public domain fingerprint datasets provide comprehensive empirical support for
the merits of our method. Further, our method finds minutiae sets that are
better in terms of precision and recall in comparison with state-of-the-art on
these two datasets. Given the lack of annotated fingerprint datasets with
minutiae ground truth, the proposed approach to robust minutiae detection will
be useful to train network-based fingerprint matching algorithms as well as for
evaluating fingerprint individuality at scale. MinutiaeNet is implemented in
Tensorflow: https://github.com/luannd/MinutiaeNetComment: Accepted to International Conference on Biometrics (ICB 2018
Deep Slap Fingerprint Segmentation for Juveniles and Adults
Many fingerprint recognition systems capture four fingerprints in one image.
In such systems, the fingerprint processing pipeline must first segment each
four-fingerprint slap into individual fingerprints. Note that most of the
current fingerprint segmentation algorithms have been designed and evaluated
using only adult fingerprint datasets. In this work, we have developed a
human-annotated in-house dataset of 15790 slaps of which 9084 are adult samples
and 6706 are samples drawn from children from ages 4 to 12. Subsequently, the
dataset is used to evaluate the matching performance of the NFSEG, a slap
fingerprint segmentation system developed by NIST, on slaps from adults and
juvenile subjects. Our results reveal the lower performance of NFSEG on slaps
from juvenile subjects. Finally, we utilized our novel dataset to develop the
Mask-RCNN based Clarkson Fingerprint Segmentation (CFSEG). Our matching results
using the Verifinger fingerprint matcher indicate that CFSEG outperforms NFSEG
for both adults and juvenile slaps. The CFSEG model is publicly available at
\url{https://github.com/keivanB/Clarkson_Finger_Segment
Facilitating sensor interoperability and incorporating quality in fingerprint matching systems
This thesis addresses the issues of sensor interoperability and quality in the context of fingerprints and makes a three-fold contribution. The first contribution is a method to facilitate fingerprint sensor interoperability that involves the comparison of fingerprint images originating from multiple sensors. The proposed technique models the relationship between images acquired by two different sensors using a Thin Plate Spline (TPS) function. Such a calibration model is observed to enhance the inter-sensor matching performance on the MSU dataset containing images from optical and capacitive sensors. Experiments indicate that the proposed calibration scheme improves the inter-sensor Genuine Accept Rate (GAR) by 35% to 40% at a False Accept Rate (FAR) of 0.01%. The second contribution is a technique to incorporate the local image quality information in the fingerprint matching process. Experiments on the FVC 2002 and 2004 databases suggest the potential of this scheme to improve the matching performance of a generic fingerprint recognition system. The final contribution of this thesis is a method for classifying fingerprint images into 3 categories: good, dry and smudged. Such a categorization would assist in invoking different image processing or matching schemes based on the nature of the input fingerprint image. A classification rate of 97.45% is obtained on a subset of the FVC 2004 DB1 database
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