36 research outputs found
Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints
The effectiveness of fingerprint-based authentication systems on good quality
fingerprints is established long back. However, the performance of standard
fingerprint matching systems on noisy and poor quality fingerprints is far from
satisfactory. Towards this, we propose a data uncertainty-based framework which
enables the state-of-the-art fingerprint preprocessing models to quantify noise
present in the input image and identify fingerprint regions with background
noise and poor ridge clarity. Quantification of noise helps the model two
folds: firstly, it makes the objective function adaptive to the noise in a
particular input fingerprint and consequently, helps to achieve robust
performance on noisy and distorted fingerprint regions. Secondly, it provides a
noise variance map which indicates noisy pixels in the input fingerprint image.
The predicted noise variance map enables the end-users to understand erroneous
predictions due to noise present in the input image. Extensive experimental
evaluation on 13 publicly available fingerprint databases, across different
architectural choices and two fingerprint processing tasks demonstrate
effectiveness of the proposed framework.Comment: IJCNN 2021 (Accepted
Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning
A fingerprint region of interest (roi) segmentation algorithm is designed to
separate the foreground fingerprint from the background noise. All the learning
based state-of-the-art fingerprint roi segmentation algorithms proposed in the
literature are benchmarked on scenarios when both training and testing
databases consist of fingerprint images acquired from the same sensors.
However, when testing is conducted on a different sensor, the segmentation
performance obtained is often unsatisfactory. As a result, every time a new
fingerprint sensor is used for testing, the fingerprint roi segmentation model
needs to be re-trained with the fingerprint image acquired from the new sensor
and its corresponding manually marked ROI. Manually marking fingerprint ROI is
expensive because firstly, it is time consuming and more importantly, requires
domain expertise. In order to save the human effort in generating annotations
required by state-of-the-art, we propose a fingerprint roi segmentation model
which aligns the features of fingerprint images derived from the unseen sensor
such that they are similar to the ones obtained from the fingerprints whose
ground truth roi masks are available for training. Specifically, we propose a
recurrent adversarial learning based feature alignment network that helps the
fingerprint roi segmentation model to learn sensor-invariant features.
Consequently, sensor-invariant features learnt by the proposed roi segmentation
model help it to achieve improved segmentation performance on fingerprints
acquired from the new sensor. Experiments on publicly available FVC databases
demonstrate the efficacy of the proposed work.Comment: IJCNN 2021 (Accepted
Active 3-D Object Recognition using AppearanceBased Aspect Graphs
We present a new active active recognition scheme (using an uncalibrated camera) based on a new idea, appearancebased aspect graphs. The scheme is robust to background clutter, and affine transformations of the object. We use a probabilistic reasoning framework which helps in probability calculations and planning the next view (when a view of the object does not contain sufficient features to recognise it unambiguously), in conjunction with a new hierarchical knowledge representation scheme. Preliminary experiments with the system show encouraging results. 1
Fast and Robust Projective Matching for Fingerprints using Geometric Hashing
Fingerprint matching is the most important module in automatic person recognition using fingerprints. We model the nonlinear distortions and noise obtained during the fingerprint capture process as transformations in the projective domain. Matching of the fingerprints involves computing the homography matrix for the projective transformation, mapping of the minutia points by this homography and finally computing the points that match each other within a pre-determined threshold. We perform a fast match using a Geometric Hashing-based algorithm which matches the points in polynomial time. Preliminary experiments with a sample representative database show promising results. 1