Signature recognition

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

Biometrics: enhancing security

Abstract: Biometrics refers to the Automated Recognition of Individuals based on their physiological or behavioral traits like voice, signature, face, iris, retina and many more. It uses the biometric characteristics of an individual that are Unique and cannot be imitated. A basic biometric system is made-up of a sensor to capture the biometric characteristics, a computer unit to process and save biometric data and an application, for which the user’s authentication is necessary. To carry out the authentication process a biometric system works in three modes. At a fundamental level, the matching algorithm returns a number that indicates the degree of similarity or dissimilarity between the two biometric samples. Biometrics is improving rapidly, and its integrators and resellers are leading the way.

Towards a Robust Physics-Based Object Recognition System

Abstract: A successful 3D object recognition system must take into account imperfections in the input data, due for example to fragmentation or sensor noise. In this paper we propose a methodology for robust 3D object recognition using uncertain image data. In particular, we present a method capable of achieving acceptable performance in the presence of both segmentation problems and sensor uncertainty, thus eliminating the need for ad hoc heuristics. The proposed method is based upon the use of probabilistic models suggested by the underlying physics processes. These models are statistically validated and tested under controlled experimentation.

A new learning scheme for the recognition of dynamical handwritten characters

Abstract: Vector comparison is essential in pattern recognition. Numerous methods based on distance computation are available to carry out such comparison. Unfortunately most of them are applicable only if the vectors are of the same length or do not take into account components misalignment. This paper presents a new distance between two representations called the elastic distance and based on the dynamic programming technique. Properties are studied. We show that it leads to a variant of the least vector quantisation technique that learns the best representants of a group of prototypes. A new centroid computation algorithm is proposed. Finally, the learning scheme algorithm has been successfully applied on an online numerical handwritten character recognition problem using a previously computed centroid of a set of prototypes.

Particle DynamicsWarping Approach for Offline Signature Recognition

Abstract: Offline signature recognition is an important form of biometric identification that can be used for various purposes. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. In this paper we explore a novel approach for reducing the variability associated with matching signatures based on curve warping. Existing techniques, such as the dynamic time warping approach, address this problem by minimizing a cost function through dynamic programming. This is by nature a one dimensional optimization process that is possible when a one dimensional parametrization of the curves is known. In this paper we propose a novel approach for solving the curve correspondence problem that is not limited by the requirement of one dimensional parametrization. The proposed approach utilizes particle dynamics and minimizes a cost function through an iterative solution of a system of first order ordinary differential equations. The proposed approach is therefore capable of handling complex curves for which a simple parametrization is not available. The proposed approach is evaluated by measuring the precision and recall rates of documents based on signature similarity. To facilitate a realistic evaluation, the signature data we use was collected from real world documents spanning a period of several decades.

Recognition of non-speech sounds using Mel-frequency cepstrum coefficients and dynamic time warping method

Abstract: With the developing technology, speech recognition systems are getting more space in our daily lives. Sounds in our environment are not only pure speech. Because of this, it is important for cochlear implants, unmanned vehicles and security systems to be able to recognize other sounds. In this work, Mel-frequency cepstrum coefficients, one of the most widely used methods for feature extraction in speech recognition, applied to various nature and animal sounds. Because each sound does not have the same duration, dynamic time warping, one of the methods used in speech recognition, is preferred to classify the feature vectors. The difference in durations of sounds affects the lengths of the feature vectors. With dynamic time warping method, one can overcome these differences. One reference record and 10 test records obtained from 10 different sound sources. True classification rate is found as 88%.