Abstract
Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.
Highlights
LITERATURE SURVEYIt has been more than a century that fingerprints have been used as a reliable biometric in person identification (Lee and Gaensslen, 1991; Newham, 1995)
We have reported our results on FVC2002 (FVC2002, 2012), FVC2004 (FVC2004, 2012) plain fingerprint, and NIST SD27 (Garris and Mccabe, 2000) latent fingerprint databases
The similarity score is calculated as the maximum number of minutiae matching pairs. It is given by the formula, NIST SD27 is a criminal fingerprint database and contains 258 fingerprint images obtained from 258 persons
Summary
It has been more than a century that fingerprints have been used as a reliable biometric in person identification (Lee and Gaensslen, 1991; Newham, 1995). Any fingerprint recognition system can deal with the presence of few spurious minutiae, but matching performance is affected when the system fails to extract a sufficient number of reliable minutiae from the latent fingerprints which already contain partial ridge information To overcome this problem we further enhance the image using FFT filter banks. After the fingerprints are enhanced using the FFT enhanced method (as discussed in section Automated Latent Fingerprint Pre-processing and Enhancement Using DCNN and FFT Filters), the broken ridges become connected and it removes false minutiae from fingerprints when extracted The similarity score is calculated as the maximum number of minutiae matching pairs It is given by the formula, NIST SD27 is a criminal fingerprint database and contains 258 fingerprint images obtained from 258 persons.
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