Signature recognition

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

Feature Representation for Online Signature Verification.

Abstract: Biometrics systems have been used in a wide range of applications and have improved people authentication. Signature verification is one of the most common biometric methods with techniques that employ various specifications of a signature. Recently, deep learning has achieved great success in many fields, such as image, sounds and text processing. In this paper, deep learning method has been used for feature extraction and feature selection.

System and method for automatic voice recognition using mapping

Abstract: A method and system that combines voice recognition engines and resolves differences between the results of individual voice recognition engines using a mapping function. Speaker independent voice recognition engines and speaker-dependent voice recognition engines are combined. Hidden Markov Model (HMM) engines and Dynamic Time Warping (DTW) engines are combined.

Dynamic Programming Algorithms in Speech Recognition

Abstract: In a system of speech recognition containing words, the recognition requires the comparison between the entry signal of the word and the various words of the dictionary. The problem can be solved efficiently by a dynamic comparison algorithm whose goal is to put in optimal correspondence the temporal scales of the two words. An algorithm of this type is Dynamic Time Warping. This paper presents two alternatives for implementation of the algorithm designed for recognition of the isolated words.

Signature Neural Networks: Definition and Application to Multidimensional Sorting Problems

Abstract: In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: (1) neural signatures to identify each unit in the network; (2) local discrimination of input information during the processing; and (3) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

Handwritten signature recognition in mobile scenarios: Performance evaluation

Abstract: Following the idea of improving our previous work on dynamic handwritten signature recognition on portable devices, a performance evaluation in a mobile scenario was done. A database with 11 users and 8 mobile devices (using stylus and finger) has been collected in order to study different parameters such as screen size, operative system and the interoperability between the devices. The evaluation was divided by 3 sessions of 20 signatures per device each; 20 skilled forgeries signatures per user were used also. The devices used were mobile phones, tablets, laptops and two specific devices for signing. The algorithm used to assess the signatures was a DTW-based signature recognition algorithm.