11,141 research outputs found
Fingerprint recognition: A study on image enhancement and minutiae extraction
Fingerprints are a great source for identification of individuals. Fingerprint recognition is one of the oldest forms of biometric identification. However obtaining a good fingerprint image is not always easy. So the fingerprint image must be preprocessed before matching. The objective of this project is to present a better and enhanced fingerprint image.
We have studied the factors relating to obtaining high performance feature points detection algorithm, such as image quality, segmentation, image enhancement and feature detection. Commonly used features for improving fingerprint image quality are Fourier spectrum energy, Gabor filter energy and local orientation. Accurate segmentation of fingerprint ridges from noisy background is necessary. For efficient enhancement and feature extraction algorithms, the segmented features must be void of any noise.
A preprocessing method consisting of field orientation, ridge frequency estimation, Gabor filtering, segmentation and enhancement is performed. The obtained image is applied to a thinning algorithm and subsequent minutiae extraction. The methodology of image preprocessing and minutiae extraction is discussed. The simulations are performed in the MATLAB environment to evaluate the performance of the implemented algorithms. Results and observations of the fingerprint images are presented at the end
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
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