Signature recognition

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

An speech and face fusion recognition method based on fuzzy integral

Abstract: Studies show that multiple modal biometric systems for small-scale populations perform better than single modal biometric systems for robots's recognition. This paper establishes a new fusion method for multiple biometric feature identification which combines visual with auditory information. Before the fusion, speaker recognition based on vector quantization and face recognition based on sparse representation get their single modal similarity and accepted rate respectively, then the similarity is normalized to get the matching score. Finally, fuzzy measure and fuzzy integrals are used for the fusion of speaker recognition and face recognition to obtain the matching result. In the actual environment, the mobile robot utilize this method to realize the recognition. The result of experiment indicates that the performance is better than single modal recognition.

Implementation of Signature Recognitionby Using Backpropagation

Abstract: Many things are required by all parties, especially in the process of recognition of one's identity, ranging from health care, maintenance of bank accounts, aviation services, immigration and others.Many ways of proving one's identity and the most popular one is using a signature.The signature is used as an identification system which serves to recognize a person's identity.Recognition process is still done manually by matching the signature by the person concerned.Therefore, the very need for a system that is able to analyze and identify the characteristics of the signature, so it can be used as an alternative to simplify the process of introducing people’s signature.Artificial neural networks can be used as one of the solutions in identification of signatures.Artificial neural network is a branch of science of artificial intelligence that is capable of processing information with the performance of certain characteristics.Artificial neural networks have some method such as perceptron, Hopfield discrete, Adaline, Backpropagation, and Kohonen.In this paper, the artificial neural network with back propagation method is applied in the process of signature and pattern recognition which provided a solution that is able to analyze and recognize people's signature.Implementation of the application of neural networks in pattern recognition signature can further be applied to any computer that handles problems in the process of matching one's data. Keywords: Neural network;Backpropagation; Identification; Signature.

Improved face recognition algorithm using extended vector quantization histogram features

Abstract: In this paper, we propose an improved face recognition approach based on the combination of Vector Quantization (VQ) and Markov Stationary Feature (MSF) which obtain the extended MSF-VQ features from facial sub-regions for face recognition. It can not only utilize the MSF framework to extend the VQ histogram based features with the spatial structure information but can also incorporate more location information extracted from different facial sub-regions so as to improve the accuracy of face recognition system. We demonstrate our proposed algorithm utilizing FB category of FERET face database and the maximum top1 recognition rate of 97.6% is obtained.

A bread recognition system using RGB-D sensor

Abstract: To improve object recognition accuracy, we introduce RGB-D sensor to get depth features and we construct two-stage recognition which combine recognition method with different characteristics. Experimental result show that the combination of these two refinement is effective to distinguish similar objects.

Dynamic signature verification for secure retrieval of classified information

Abstract: With the growth of web enabled services and e-commerce, tremendous amount of information is now readily available on the Internet. A large proportion of this is classified information, which has to be protected against unauthorized access. Password or PIN can be used in conjunction with digital signature, for verification of the identity of users. This paper proposes a dynamic handwritten signature verification based access control system that can be employed in the legal, banking and commercial domains for designing secure information retrieval systems. The dynamic handwritten signature in this system is captured by using a digital tablet or PDA (Personal Digital Assistant) with contact sensitive acquisition system. After preprocessing, the signature data is compared with the templates of authorized signatures by employing an innovative neuro-fuzzy pattern recognition system based on sensing the pressure variable and total time required for executing the signature for uniquely identifying the potential user. The error in matching is used to arrive at the decision regarding permission or denial of access to the classified document. The neuro-fuzzy technique applied in the dynamic signature system is based on evolving fuzzy neural network. This technique has been tested on signatures drawn from signature verification competition database obtained from the internet. Encouraging results show that this technique is a good candidate for the development of practical applications.