17 research outputs found
Learning Representations from Persian Handwriting for Offline Signature Verification, a Deep Transfer Learning Approach
Offline Signature Verification (OSV) is a challenging pattern recognition
task, especially when it is expected to generalize well on the skilled
forgeries that are not available during the training. Its challenges also
include small training sample and large intra-class variations. Considering the
limitations, we suggest a novel transfer learning approach from Persian
handwriting domain to multi-language OSV domain. We train two Residual CNNs on
the source domain separately based on two different tasks of word
classification and writer identification. Since identifying a person signature
resembles identifying ones handwriting, it seems perfectly convenient to use
handwriting for the feature learning phase. The learned representation on the
more varied and plentiful handwriting dataset can compensate for the lack of
training data in the original task, i.e. OSV, without sacrificing the
generalizability. Our proposed OSV system includes two steps: learning
representation and verification of the input signature. For the first step, the
signature images are fed into the trained Residual CNNs. The output
representations are then used to train SVMs for the verification. We test our
OSV system on three different signature datasets, including MCYT (a Spanish
signature dataset), UTSig (a Persian one) and GPDS-Synthetic (an artificial
dataset). On UT-SIG, we achieved 9.80% Equal Error Rate (EER) which showed
substantial improvement over the best EER in the literature, 17.45%. Our
proposed method surpassed state-of-the-arts by 6% on GPDS-Synthetic, achieving
6.81%. On MCYT, EER of 3.98% was obtained which is comparable to the best
previously reported results
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
Offline signature verification using DAG-CNN
This paper presents the implementation of a DAG-CNN which aims to classify and verify the authenticity of the offline signatures of 3 users, using the writer-independent method. In order to develop this work, 2 databases (training / validation and testing) were built manually, i.e. the manual collection of the signatures of the 3 users as well as forged signatures made by people not belonging to the base and altered by the same users were done, and signatures of another 115 people were used to create the category of non-members. Once the network is trained, its validation and subsequent testing is performed, obtaining overall accuracies of 99.4% and 99.3%, respectively, showing the features learned by the network and verifying the ability of this configuration of neural network to be used in applications for identification and verification of offline signatures
Active Transfer Learning for Persian Offline Signature Verification
Offline Signature Verification (OSV) remains a challenging pattern
recognition task, especially in the presence of skilled forgeries that are not
available during the training. This challenge is aggravated when there are
small labeled training data available but with large intra-personal variations.
In this study, we address this issue by employing an active learning approach,
which selects the most informative instances to label and therefore reduces the
human labeling effort significantly. Our proposed OSV includes three steps:
feature learning, active learning, and final verification. We benefit from
transfer learning using a pre-trained CNN for feature learning. We also propose
SVM-based active learning for each user to separate his genuine signatures from
the random forgeries. We finally used the SVMs to verify the authenticity of
the questioned signature. We examined our proposed active transfer learning
method on UTSig: A Persian offline signature dataset. We achieved near 13%
improvement compared to the random selection of instances. Our results also
showed 1% improvement over the state-of-the-art method in which a fully
supervised setting with five more labeled instances per user was used
Graph-Based Offline Signature Verification
Graphs provide a powerful representation formalism that offers great promise
to benefit tasks like handwritten signature verification. While most
state-of-the-art approaches to signature verification rely on fixed-size
representations, graphs are flexible in size and allow modeling local features
as well as the global structure of the handwriting. In this article, we present
two recent graph-based approaches to offline signature verification: keypoint
graphs with approximated graph edit distance and inkball models. We provide a
comprehensive description of the methods, propose improvements both in terms of
computational time and accuracy, and report experimental results for four
benchmark datasets. The proposed methods achieve top results for several
benchmarks, highlighting the potential of graph-based signature verification
DIGITAL IDENTITY MODELLING FOR DIGITAL FINANCIAL SERVICES IN ZAMBIA
Identification and verification have always been at the heart of financial services and payments, which is even more the case in the digital age. So, while banks have long been trusted to keep money safe, is there a new role for them as stewards of digital identity? Governments should, in consultation with the private sector, develop a national identity strategy based on a federated-style model in which public and private sector identity providers would compete to supply trusted digital identities to individuals and businesses. Back then, when the world seemed smaller, slower and more local, physical identity documents were adequate for face-to-face transactions. However, the Internet changed everything. It shrank distances, created new business models and generally sped everything up. From the innovation lifecycle to access to information, processes and the clock-speed on risk, the Internet has accelerated everything. The use of Internet in doing business has grown over the years in Africa and Zambia in particular. As such, the incidences of online identity theft have grown too. Identity theft is becoming a prevalent and increasing problem in Zambia. An identity thief only requires certain identity information to decimate a victim's life and credit. This research proposes to identify and extract various forms of identity attributes from various sources used in the physical and cyberspace to identity users accessing the financial services through extracting identity attributes from the various forms of identity credentials and application forms. Finally, design a digital identity model based on Shannon鈥檚 Information theory and Euclidean metric based Euclidean Distance Geometry (EDG) to be used for quantifying, implementation and validating of extracted identity attributes from various forms of identity credentials and application forms, in an effective way