Signature recognition

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

Survey on Offline Signature Recognition Techniques

Abstract: Digital Recognition of any individual is an under kind to recognize the people. Human identification utilizing. As signatures are widely accepted bio-metric for authentication and identification of a person because every person has a distinct signature with its specific behavioural property, so it is very much necessary to prove the authenticity of signature itself. A huge increase in forgery cases relative to signatures induced a need of efficient “Signature Verification System”. These systems can be online or offline based on type of input taken by the system. This paper represents a brief review on various approaches used in signature verification systems. In this paper we have purposed a method to enhance the security level in signature recognition and to detect the true and false users. Calculate distance (D) b/n Input sign and among present in database and Threshold. Keywords— Signature Recognition, Off-line Signature Recognition and Verification, SVM, SFTA, FAR, FRR

Research on the BP-network-based Iris Recognition

Abstract: Iris recognition is the highly trusted identification recognition technology among methods of biological recognition. In this paper, we use the back propagation algorithm to train the neural network, so as to establish the iris recognition system model. The experiment demonstrates that it has a high recognition rate and the recognition speed is reasonable. The proposed method provides a convenient way for iris recognition.

Analysis of Feature Recognition of Neural Network Method in the String Recognition

Abstract: This paper aims that analysing neural network method in pattern recognition. A neural network is a processing device, whose design was inspired by the design and functioning of human brain and their components. The proposed solutions focus on applying Feature Recognition Neural Network model for pattern recognition. The primary function of which is to retrieve in a pattern stored in memory, when an incomplete or noisy version of that pattern is presented. An associative memory is a storehouse of associated patterns that are encoded in some form. In auto-association, an input pattern is associated with itself and the states of input and output units coincide. When the storehouse is incited with a given distorted or partial pattern, the associated pattern pair stored in its perfect form is recalled. Pattern recognition techniques are associated a symbolic identity with the image of the pattern. This problem of replication of patterns by machines (computers) involves the machine printed patterns. There is no idle memory containing data and programmed, but each neuron is programmed and continuously active.

Server-side reconstruction trajectory generation methods for hand written objects authentication - A comparative review

Abstract: In online applications, there is a foreseeable explosive growth in biometric personal authentication systems which are covenant with a measurable behavioral trait. Hand written object [HO] verification is the process used to recognize an individual, which is intuitive reliable indicator. Verification of a HO as a biometric modality still is a challenging field of research, as number of online and offline commercial applications use modern acquisition devices. The employment of HO verification with technology still remains open for novel methods due to inter-class and intra-class variations. This paper discusses the trajectory generation [TG] methods applicable for any HO verification, such as character recognition, handwriting verification, style classification, shape recognition and signature recognition, which are suitable for latest trend of mobile-commerce and web-commerce applications.

Neural Network Optimization for the Recognition of Persons Using the Iris Biometric Measure

Abstract: This chapter describes the application of modular neural network architecture for the recognition of persons using the human iris images [80]; the iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA). We show the results of testing the modular neural network, its optimization using genetic algorithms and the integration with the methods of gating network, type-1 fuzzy integration, and fuzzy integration optimized by genetic algorithms. Simulation results show a good identification using the fuzzy integrators and the best structure found by the genetic algorithm.