251,057 research outputs found
Efficient software attack to multimodal biometric systems and its application to face and iris fusion
This is the author’s version of a work that was accepted for publication in Pattern Recognition Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition Letters 36, (2014) DOI: 10.1016/j.patrec.2013.04.029In certain applications based on multimodal interaction it may be crucial to determine not only what the user is doing (commands), but who is doing it, in order to prevent fraudulent use of the system. The biometric technology, and particularly the multimodal biometric systems, represent a highly efficient automatic recognition solution for this type of applications.
Although multimodal biometric systems have been traditionally regarded as more secure than unimodal systems, their vulnerabilities to spoofing attacks have been recently shown. New fusion techniques have been proposed and their performance thoroughly analysed in an attempt to increase the robustness of multimodal systems to these spoofing attacks. However, the vulnerabilities of multimodal approaches to software-based attacks still remain unexplored. In this work we present the first software attack against multimodal biometric systems. Its performance is tested against a multimodal system based on face and iris, showing the vulnerabilities of the system to this new type of threat. Score quantization is afterwards studied as a possible countermeasure, managing to cancel the effects of the proposed attacking methodology under certain scenarios.This work has been partially supported by projects Contexts (S2009/TIC-1485) from CAM,
Bio-Challenge (TEC2009-11186) and Bio-Shield (TEC2012-34881) from Spanish MINECO,
TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica
Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution
Visual information extraction (VIE) has attracted considerable attention
recently owing to its various advanced applications such as document
understanding, automatic marking and intelligent education. Most existing works
decoupled this problem into several independent sub-tasks of text spotting
(text detection and recognition) and information extraction, which completely
ignored the high correlation among them during optimization. In this paper, we
propose a robust visual information extraction system (VIES) towards real-world
scenarios, which is a unified end-to-end trainable framework for simultaneous
text detection, recognition and information extraction by taking a single
document image as input and outputting the structured information.
Specifically, the information extraction branch collects abundant visual and
semantic representations from text spotting for multimodal feature fusion and
conversely, provides higher-level semantic clues to contribute to the
optimization of text spotting. Moreover, regarding the shortage of public
benchmarks, we construct a fully-annotated dataset called EPHOIE
(https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for
both text spotting and visual information extraction. EPHOIE consists of 1,494
images of examination paper head with complex layouts and background, including
a total of 15,771 Chinese handwritten or printed text instances. Compared with
the state-of-the-art methods, our VIES shows significant superior performance
on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used
SROIE dataset under the end-to-end scenario.Comment: 8 pages, 5 figures, to be published in AAAI 202
An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition
Traditionally, the performance of ocr algorithms and systems is based on the
recognition of isolated characters. When a system classifies an individual
character, its output is typically a character label or a reject marker that
corresponds to an unrecognized character. By comparing output labels with the
correct labels, the number of correct recognition, substitution errors
misrecognized characters, and rejects unrecognized characters are determined.
Nowadays, although recognition of printed isolated characters is performed with
high accuracy, recognition of handwritten characters still remains an open
problem in the research arena. The ability to identify machine printed
characters in an automated or a semi automated manner has obvious applications
in numerous fields. Since creating an algorithm with a one hundred percent
correct recognition rate is quite probably impossible in our world of noise and
different font styles, it is important to design character recognition
algorithms with these failures in mind so that when mistakes are inevitably
made, they will at least be understandable and predictable to the person
working with theComment: 6pages, 5 figure
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