Abstract. Most people are used to signing documents and because of this, it is a trusted and natural method for user identity verification, reducing the cost of password maintenance and decreasing the risk of eBusiness fraud. In the proposed system, identity is securely verified and an authentic electronic signature is created using biometric dynamic signature verification. Shape, speed, stroke order, off-tablet motion, pen pressure and timing information are captured and analyzed during the real-time act of signing the handwritten signature. The captured values are unique to an individual and virtually impossible to duplicate. This paper presents a research of various HMM based techniques for signature verification. Different topologies are compared in order to obtain an optimized high performance signature verification system and signal normalization preprocessing makes the system robust with respect to writer variability.
Abstract.A secret and secure ballot is the core of every democracy. We all feel proud of being able to decide the future of our countries by making appropriate use of our right to vote in an election. However, how can we improve the efficiency of the voting process? Democratic governments should have mechanisms which ensure the integrity, security and privacy of its citizens at the polls during an election process. This paper describes a new electronic secure voting system, based on automatic paper ballot reading, which can be utilized to offer efficient help to officials and party representatives during elections. It presents how the system is organized, it also describes our OCR system and how it is implemented to read paper ballots, and it ends showing some experimental results.
This paper addresses the problem of image annotation using a combination of visual and semantic information. Our model involves two stages: a Nearest Neighbor computation and a tag transfer stage that collects the final annotations. For the latter stage, several algorithms have been implemented in the past using labels' information or including implicitly some visual features. In this paper we propose a novel algorithm for tag transfer that takes advantage explicitly of semantic and visual information. We also present a structured training procedure based on a concept we have called Image Networking: all the images in a training database are "connected" visually and semantically, so it is possible to exploit these connections to learn the tag transfer parameters at annotation time. This learning is local for the test image and it exploits the information obtained in the Nearest Neighbor computation stage. We demonstrate that our approach achieves state-of-the-art performance on the ImageCLEF2011 dataset.
Real time interpretation of outdoor scenes for video-surveillance applications is a complex task. These kind of systems are supposed to work day and night, 365 days per year. under changing environmental conditions, variable weather and lighting conditions, with a minimum ratio of false alarms. Video-surveillance systems should be able to detect moving objects of different size moving at different speeds, and provide this information in real time to those persons who may need it at a reasonable price. This paper describes a system which provides a cost-effective solution to these kinds of problems based on the idea of processing sequences of images in real time.
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