Fingerprint Distortion Rectification using Deep Convolutional Neural Networks
This addresses a serious issue in fingerprint recognition, particularly for negative recognition scenarios where users may intentionally distort fingerprints, though it is an incremental improvement over existing methods.
The paper tackles the problem of elastic fingerprint distortion, which impairs recognition systems, by developing a deep convolutional neural network (DCNN) model that estimates distortion parameters ten times faster than previous methods and significantly improves matching performance on distorted samples.
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.