Signature recognition

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

Electronic pattern recognition

Abstract: Method and apparatus for electronic recognition of patterns as particularly adapted to the pattern tracking of the electronic signature of an object within the field of view of a television camera.

Unobtrusive multi-modal biometric recognition using activity-related signatures

Abstract: The present study proposes a novel multimodal biometrics framework for identity recognition and verification following the concept of the so called ‘on-the-move’ biometry, which sets as the final objective the non-stop authentication in an unobtrusive manner. Gait, that forms the major modality of the scheme, is complemented by new dynamic biometric signatures extracted from several activities performed by the user. Gait recognition is performed through a robust scheme that is based on geometric descriptors of gait energy images and is able to compensate for undesired gait behaviour like walking direction variations and stops. On the other hand, the biometric signatures, based on the user activities, are extracted by tracking of three points of interest and are seen to provide a powerful auxiliary biometric trait. Finally, score level fusion is performed and the experimental results illustrate that the proposed multimodal biometric scheme provides very promising results in realistic application scenarios.

Rotation, Scaling and Translation Analysis of Biometric Signature Templates

Abstract: Biometric authentication systems that make use of signature verification methods often render optimum performance only under limited and restricted conditions. Such methods utilize several training samples so as to achieve high accuracy. Moreover, several constraints are imposed on the end-user so that the system may work optimally, and as expected. For example, the user is made to sign within a small box, in order to limit their signature to a predefined set of dimensions, thus eliminating scaling. Moreover, the angular rotation with respect to the referenced signature that will be inadvertently introduced as human error, hampers performance of biometric signature verification systems. To eliminate this, traditionally, a user is asked to sign exactly on top of a reference line. In this paper, we propose a robust system that optimizes the signature obtained from the user for a large range of variation in Rotation-Scaling-Translation (RST) and resolves these error parameters in the user signature according to the reference signature stored in the database.

A multi-lingual speech recognition system using a neural network approach

Abstract: A learning vector quantization method based on the dynamic time warping scheme is proposed for the speech recognition. The optimized speech database and adequate time-alignment vector matching can be achieved. The recognition accuracy for different users is improved by adapting the speech database using the learning vector quantization method. An user-friendly software system of the proposed method is implemented to demonstrate the recognition of 200 voice commands. Various languages and dialects can be realized in our system with a high recognition accuracy. The evaluation board of speech recognition using an 8051 microprocessor has been designed for the industrial applications. A pipelined programmable micro-architecture of the speech recognition processor is developed for the high-performance and high-speed recognition system.

Off-line signature verification systems: Recent advances

Abstract: We present in this paper a state of the latest advances in the field of offline handwritten signature verification We describe the main approaches that have been proposed in the recent decades Besides, we introduce the database of static signatures published in the literature as well as international competitions organized in the domain Also, we present our contribution in the field