A survey on fingerprint minutiae-based local matching for verification and identification: Taxonomy and experimental evaluation

Abstract

Fingerprint recognition has found a reliable application for verification or identification of people in biometrics. Globally, fingerprints can be viewed as valuable traits due to several perceptions observed by the experts; such as the distinctiveness and the permanence on humans and the performance in real applications. Among the main stages of fingerprint recognition, the automated matching phase has received much attention from the early years up to nowadays. This paper is devoted to review and categorize the vast number of fingerprint matching methods proposed in the specialized literature. In particular, we focus on local minutiae-based matching algorithms, which provide good performance with an excellent trade-off between efficacy and efficiency. We identify the main properties and differences of existing methods. Then, we include an experimental evaluation involving the most representative local minutiae-based matching models in both verification and evaluation tasks. The results obtained will be discussed in detail, supporting the description of future directions.

Modified Gabor Filtration and Edge Based Convolution in Neural Network

“…Daniel Peralta et al, 2015 [9] Fingerprint recognition had found a reliable application for verification or identification of 32 people in biometrics. Worldwide, fingerprints can be viewed as respected traits due to several 33 perceptions observed by the experts; such as the distinctiveness and the permanence on 34 humans and the performance in real applications.…”

“…using the frequency domain for estimation of local orientation and ridge distance. Buades et al proposed a decomposition of the signal of the fingerprint image into two components [9]. Local Total Variation is used as a measure for sensibility regarding smoothing operations.…”

DPD-DFF: A dual phase distributed scheme with double fingerprint fusion for fast and accurate identification in large databases

“…Although there are several ways to perform a matching between two fingerprints, many matching algorithms use the minutiae [3,7,29], comparing two minutiae sets to return a similarity score. The matching is performed once for each comparison between two fingerprints.…”

“…When two fingerprints are to be compared, the minutiae are extracted from the images, and then a matching algorithm is applied over the two minutiae sets to determine a similarity level. There are multiple proposals of minutiae-based matching algorithms in the literature [7]. Some of them are very efficient due to their simplicity [8], while others are very accurate [9].…”

An Accelerated Architecture Based on GPU and Multi-Processor Design for Fingerprint Recognition

“…There are different methods in literature that lets Minutiae matching based on types of features [29][30] [31] or on correlations of image [28].…”