2,229 research outputs found
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
Pickup usability dominates: a brief history of mobile text entry research and adoption
Text entry on mobile devices (e.g. phones and PDAs) has been a research challenge since devices shrank below laptop size: mobile devices are simply too small to have a traditional full-size keyboard. There has been a profusion of research into text entry techniques for smaller keyboards and touch screens: some of which have become mainstream, while others have not lived up to early expectations. As the mobile phone industry moves to mainstream touch screen interaction we will review the range of input techniques for mobiles, together with evaluations that have taken place to assess their validity: from theoretical modelling through to formal usability experiments. We also report initial results on iPhone text entry speed
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
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