In this paper, we propose a new Automatic Target Recognition (ATR) system, based on Deep Convolutional Neural Network (DCNN), to detect the targets in Forward Looking Infrared (FLIR) scenes and recognize their classes. In our proposed ATR framework, a fully convolutional network (FCN) is trained to map the input FLIR imagery data to a fixed stride correspondingly-sized target score map. The potential targets are identified by applying a threshold on the target score map. Finally, corresponding regions centered at these target points are fed to a DCNN to classify them into different target types while at the same time rejecting the false alarms. The proposed architecture achieves a significantly better performance in comparison with that of the state-of-the-art methods on two large FLIR image databases.
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Fingerprint Distortion Rectification using Deep Convolutional Neural Networks
Elastic distortion of fingerprints has a negative effect on
the performance of fingerprint recognition systems. This
negative effect brings inconvenience to users in authentication
applications. However, in the negative recognition
scenario where users may intentionally distort their fingerprints,
this can be a serious problem since distortion will
prevent recognition system from identifying malicious users.
Current methods aimed at addressing this problem still have
limitations. They are often not accurate because they estimate
distortion parameters based on the ridge frequency
map and orientation map of input samples, which are not
reliable due to distortion. Secondly, they are not efficient
and requiring significant computation time to rectify samples.
In this paper, we develop a rectification model based
on a Deep Convolutional Neural Network (DCNN) to accurately
estimate distortion parameters from the input image.
Using a comprehensive database of synthetic distorted
samples, the DCNN learns to accurately estimate distortion
bases ten times faster than the dictionary search methods
used in the previous approaches. Evaluating the proposed
method on public databases of distorted samples shows that
it can significantly improve the matching performance of
distorted samples.
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- NSF-PAR ID:
- 10053532
- Date Published:
- Journal Name:
- The 11th IAPR International Conference on Biometrics (ICB 2018)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
