Topic
Signature recognition
About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.
Papers published on a yearly basis
Papers
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13 Jul 2006TL;DR: A new global feature for on-line signature verification, named chain code distance (CCD) represents signature shape using the vector quantization, which is expected to increase the performance of the verification system when it is used with other methods in future works.
Abstract: As reported in many researches, the performance of signature verification is quite high in these days. However, as error rate reduces making performance higher cost more efforts and is getting difficult. This paper proposes a new global feature for on-line signature verification, which can be used in addition to the other system. The proposed feature, named chain code distance (CCD) represents signature shape using the vector quantization. A signature is divided into several segmentation and feature points are selected through the process. Then proposed feature is calculated by extracting information from each segment. Experimental results show that the proposed feature can be used as a global feature. The number of included genuine signatures increases rapidly as the threshold increases, but the number of forged signature increases linearly. The experimental result was remarkable for English signature and the result for Japanese signature was acceptable also. Proposed global feature is expected to increase the performance of the verification system when it is used with other methods in future works.
1 citations
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01 May 2017TL;DR: This work introduces a method capable of detecting degraded features in biometric signatures by exploiting feature correlation, and performs a redundancy analysis of non-degraded data to build an undirected graphical model, whose energy minimization determines the sequence of degraded components of the biometric sample.
Abstract: An error-correcting code (ECC) is a process of adding redundant data to a message, such that it can be recovered by a receiver even if a number of errors are introduced in transmission. Inspired by the principles of ECC, we introduce a method capable of detecting degraded features in biometric signatures by exploiting feature correlation. The main novelty is that, unlike existing biometric cryptosystems, the proposed method works directly on the biometric signature. Our approach performs a redundancy analysis of non-degraded data to build an undirected graphical model (Markov Random Field), whose energy minimization determines the sequence of degraded components of the biometric sample. Experiments carried out in different biometric traits ascertain the improvements attained when disregarding degraded features during the matching phase. Also, we stress that the proposed method is general enough to work in different classification methods, such as CNNs.
1 citations
01 Jan 2006
TL;DR: A technique for estimating the frame length, frame overlap and HMM topol-ogy from a single, clean, example animal vocalization is proposed and provides reasonable estimates for theframe length, the frame overlap, and the HMMTopology, given the quality of the example vocalizations.
Abstract: Preface Automatic Speech Recognition (ASR) is a useful tool that can facilitate the research and study of animal vocalizations. The use of human speech-based signal processing techniques for animal vocalizations has several pitfalls. Animal vocalizations may not share the same spectral or temporal characteristics as human speech. As a result , the typical ASR assumptions concerning the best frame length, frame overlap and HMM topology may not be suitable for various animal vocalizations. This paper proposes a technique for estimating the frame length, frame overlap and HMM topol-ogy from a single, clean, example animal vocalization. Multiple trials are run using the proposed technique, against the vocalizations of two distinct animal species: the Norwegian Ortolan Bunting (Emberiza Hortulana) and the African Elephant (Lox-odonta Africana). The results are examined, and the technique provides reasonable estimates for the frame length, the frame overlap and the HMM topology, given the quality of the example vocalizations. Specific recommendations are made for the continuation of this research into a usable tool for animal researches. ii Acknowledgments I thank all of the people that made this work possible; including, but not limited to, the following people: To my wife, Denise, for her love and support and for graciously accepting my absence from our living room every evening for the last year. To my children, Logan and Zoe, for gracing our lives. To my parents, James and Kathleen, and to the Holy Trinity, for providing me with the gifts that make my life's work possible. Finally, I thank Sun Tzu, for teaching me how to live purposefully: " Withdraw like a mountain in movement, advance like a rainstorm. Strike and crush with shattering force; go into battle like a tiger. " [1] iii iv I dedicate this work to my wife Denise, my son Logan and my daughter Zoe, whom are living examples of courage, unbounded energy and enthusiasm, respectively.
1 citations
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05 Jun 1988TL;DR: The author discusses some of the properties of vertex space, including insensitivity to changes in scale, orientation, and partial object occlusion, and how these properties relate to problems in model-based object recognition.
Abstract: The author discusses some of the properties of vertex space, including insensitivity to changes in scale, orientation, and partial object occlusion, and how these properties relate to problems in model-based object recognition. He also describes techniques developed for 2-D and 3-D object recognition using vertex space. The vertex-space approach to object recognition is powerful and efficient, deals well with missing information, and does not require conventional region segmentation. Results to date and an indication of future research directions are presented. >
1 citations
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TL;DR: This paper presents a unique and novel pre-processing method for multi-dimensional and non-uniform data with the aim of making it uniform and reduced in size without losing much of its value.
Abstract: We are in the era of data analytics and data science which is on full bloom. There is abundance of all kinds of data for example biometrics based data, satellite images data, chip-seq data, social network data, sensor based data etc. from a variety of sources. This data abundance is the result of the fact that storage cost is getting cheaper day by day, so people as well as almost all business or scientific organizations are storing more and more data. Most of the real data is multi-dimensional, non-uniform, and big in size, such that it requires a unique pre-processing before analyzing it. In order to make data useful for any kind of analysis, pre-processing is a very important step. This paper presents a unique and novel pre-processing method for multi-dimensional and non-uniform data with the aim of making it uniform and reduced in size without losing much of its value. We have chosen biometric signature data to demonstrate the proposed method as it qualifies for the attributes of being multi-dimensional, non-uniform and big in size. Biometric signature data does not only captures the structural characteristics of a signature but also its behavioral characteristics that are captured using a dynamic signature capture device. These features like pen pressure, pen tilt angle, time taken to sign a document when collected in real-time turn out to be of varying dimensions. This feature data set along with the structural data needs to be pre-processed in order to use it to train a machine learning based model for signature verification purposes. We demonstrate the success of the proposed method over other methods using experimental results for biometric signature data but the same can be implemented for any other data with similar properties from a different domain.
1 citations