2,436 research outputs found
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation
Latent fingerprint identification is attracting increasing interest because of its important role
in law enforcement. Although the use of various fingerprint features might be required for successful latent
fingerprint identification, methods based on minutiae are often readily applicable and commonly outperform
other methods. However, as many fingerprint feature representations exist, we sought to determine if the
selection of feature representation has an impact on the performance of automated fingerprint identification
systems. In this paper, we review the most prominent fingerprint feature representations reported in the
literature, identify trends in fingerprint feature representation, and observe that representations designed for
verification are commonly used in latent fingerprint identification. We aim to evaluate the performance of
the most popular fingerprint feature representations over a common latent fingerprint database. Therefore,
we introduce and apply a protocol that evaluates minutia descriptors for latent fingerprint identification
in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve. From our
experiments, we found that all the evaluated minutia descriptors obtained identification rates lower than
10% for Rank-1 and 24% for Rank-100 comparing the minutiae in the database NIST SD27, illustrating
the need of new minutia descriptors for latent fingerprint identification.This work was supported in part by the National Council of Science and Technology of Mexico (CONACYT) under Grant PN-720 and
Grant 63894
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