Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Handwritten signature identification using basic concepts of graph theory

Abstract: Handwritten signature is being used in various applications on daily basis. The problem arises when someone decides to imitate our signature and steal our identity. Therefore, there is a need for adequate protection of signatures and a need for systems that can, with a great degree of certainty, identify who is the signatory. This paper presents previous work in the field of signature and writer identification to show the historical development of the idea and defines a new promising approach in handwritten signature identification based on some basic concepts of graph theory. This principle can be implemented on both on-line handwritten signature recognition systems and off-line handwritten signature recognition systems. Using graph norm for fast classification (filtration of potential users), followed by comparison of each signature graph concepts value against values stored in database, the system reports 94.25% identification accuracy.

A Chaotic Theorectical Approach to ECG-Based Identity Recognition [Application Notes]

Abstract: Sophisticated technologies realized from applying the idea of biometric identification are increasingly applied in the entrance security management system, private document protection, and security access control Common biometric identification involves voice, attitude, keystroke, signature, iris, face, palm or finger prints, etc Still, there are novel identification technologies based on the individual's biometric features under development [1-4]

Quantitative evaluation of the effects of aging on biometric templates

Abstract: The long-term performance of biometric authentication systems is highly depended on the permanence of biometric features stored in biometric templates. Aging variation causes modifications on biometric features that affect the matching between stored and captured biometric templates causing in that way deterioration in the performance of biometric authentication systems. In this study the authors attempt to quantify the effects of aging for different biometric modalities, so that it is possible to draw conclusions related to the effect of aging on different types of biometric templates. In this context variations between distributions containing biometric features from different age groups are quantified, allowing in that way the definition of age-sensitive and age-invariant biometric features. An important aspect of the proposed approach is the standardised and generic nature of the approach that allows the derivation of comparative results between different modalities and different feature vectors. The work presented in this study provides a valuable tool for selecting, either age-invariant features for use in identity authentication applications, or for selecting age-sensitive features for age-estimation-related applications.

Character recognition with selective attention

Abstract: Selective attention is one of the most essential mechanisms for visual pattern recognition. One of the authors had previously proposed a model of selective attention, which has a function of segmenting patterns, as well as the function of recognizing patterns. The idea of this selective attention model can be extended to be used for several applications. The structure of the model used for connected character recognition is discussed. The authors offer two examples of its applications. One is the recognition and segmentation of connected characters in cursive handwriting of English words. Another example is the recognition of Chinese characters. Preliminary experiments with computer simulation, in which only a small number of characters have been taught to the models, show that this approach is promising for both of the applications. >

Identification of Individuals using Electrocardiogram

Abstract: Protection anxiety is to be increased as the technology for forgery grows. Reliable personal Identification and prevention of forged identities is one of the major tasks. Currently, Biometrics is being used extensively for the purpose of security measures. Biometric recognition provides strong security by identifying an individual based on the feature vector(s) derived from their physiological and/or behavioral characteristics. It has been proved that the human Electrocardiogram (ECG) shows adequately unique patterns for biometric recognition. Individual can be identified once ECG signature is formulated. This paper presents a systematic Template matching for Identification of individuals from ECG data. This work establishes that ECG signal is a signature like fingerprint, retinal signature for any individual Identification. Samples of individuals from the MIT/BIH database were taken. The matching decisions are evaluated on the basis of correlation coefficient between features. Preliminary experimental results indicate that the system is accurate (99%), robust, error rate is smaller than 0.9 and achieves a good result for Identification process.