# Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
###### Abstract  
Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning a score for each match based on the Euclidean distance between the SIFT descriptors of the two matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while discarding very few correct matches (less than 4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets demonstrate that the proposed framework delivers comparable or superior performance to several state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%. fingerprint minutiae; SIFT feature detection; feature matching; FVC2004 database; EER  
27 April 2022  
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biometric security devices highly popular in sensitive high-security areas such as banks, correctional facilities, jewellers, prisons and military establishments [2]. Feature extraction is basically a dimensionality reduction process, whose ultimate goal is to gain the most relevant information from the original data in a high-dimensional space and represent that information in a lower dimensional space. When the input data to an algorithm are too large to be processed, and it is suspected to be excrescent (many data, but not much information), then the input data should be converted into a reduced set of representative features (also called feature vector). In other words, through the process of extracting features, the input high-dimensional data are converted to a small set of low-dimensional features to identify useful information [3]. If the extracted features are carefully selected, it is highly expected that the pertinent information will be extracted from the input data by the reduced feature set, so as to effectively perform a given task by utilizing this reduced representation rather than the full-size input data. A fingerprint recognition system is an automatic pattern recognition system that typically consists of three fundamental stages: image pre-processing, feature extraction and fingerprint matching [4]. A good feature set contains rich information that can effectively distinguish an object from other objects (i.e., being able to identify an object). At this stage, it is necessary as much as possible to prevent the creation of different feature codes for the objects in the same class [5]. Latent fingerprints are merely partial impressions of the finger's ridge pattern inadvertently left after fingertips contact a surface, which involve ridges and valleys. In a fingerprint, ridges are presented as black lines, whereas the valleys are presented as a white area among the ridges [6]. Fingerprint features can be broadly divided into two categories:  
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