751 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
Duration and Interval Hidden Markov Model for Sequential Data Analysis
Analysis of sequential event data has been recognized as one of the essential
tools in data modeling and analysis field. In this paper, after the examination
of its technical requirements and issues to model complex but practical
situation, we propose a new sequential data model, dubbed Duration and Interval
Hidden Markov Model (DI-HMM), that efficiently represents "state duration" and
"state interval" of data events. This has significant implications to play an
important role in representing practical time-series sequential data. This
eventually provides an efficient and flexible sequential data retrieval.
Numerical experiments on synthetic and real data demonstrate the efficiency and
accuracy of the proposed DI-HMM
Learning recurrent representations for hierarchical behavior modeling
We propose a framework for detecting action patterns from motion sequences
and modeling the sensory-motor relationship of animals, using a generative
recurrent neural network. The network has a discriminative part (classifying
actions) and a generative part (predicting motion), whose recurrent cells are
laterally connected, allowing higher levels of the network to represent high
level phenomena. We test our framework on two types of data, fruit fly behavior
and online handwriting. Our results show that 1) taking advantage of unlabeled
sequences, by predicting future motion, significantly improves action detection
performance when training labels are scarce, 2) the network learns to represent
high level phenomena such as writer identity and fly gender, without
supervision, and 3) simulated motion trajectories, generated by treating motion
prediction as input to the network, look realistic and may be used to
qualitatively evaluate whether the model has learnt generative control rules
Arabic Handwriting Synthesis
Training and testing data for optical character recognition are cumbersome to obtain. If large amounts of data can be produced from small amounts, much time and effort can be saved. This paper presents an approach to synthesize Arabic handwriting. We segment word images into labeled characters and then use these in synthesizing arbitrary words. The synthesized text should look natural; hence, we define some criteria to decide on what is acceptable as natural-looking.
The text that is synthesized by using the natural-looking constrain is compared to text that is synthesized without using the natural-looking constrain for evaluation
Synthetic generation of handwritten signatures based on spectral analysis
Javier Galbally ; Julian Fierrez ; Marcos Martinez-Diaz ; Javier Ortega-Garcia; "Synthetic generation of handwritten signatures based on spectral analysis", Optics and Photonics in Global Homeland Security V and Biometric Technology for Human Identification VI, Proc. SPIE 7306 (May 05, 2009); doi:10.1117/12.817928. Copyright 2009 Society of Photo‑Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Proceedings of the V Optics and Photonics in Global Homeland Security; and VI Biometric Technology for Human Identification (Orlando, Florida, United States)A new method to generate synthetic online signatures is presented. The algorithm uses a parametrical model to generate the synthetic Discrete Fourier Transform (DFT) of the trajectory signals, which are then refined in the time domain and completed with a synthetic pressure function. Multiple samples of each signature are created so that synthetic databases may be produced. Quantitative and qualitative results are reported, showing that, in addition to presenting a very realistic appearance, the synthetically generated signatures have very similar characteristics to those that enable the recognition of real signatures.This work was supported by Spanish MEC under project TEC2006-13141-C03-03
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