Signature recognition

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

Using hidden Markov models at speech recognition tasks

Abstract: In this paper some questions of using of hidden Markov models at automatic speech recognition and some architectures of automatic speech recognition systems are considered.

Application of biometric identification technology for network security in the network and information era, which will greatly change the life-style of people

Abstract: The global spread revolution of information and information technology is playing a decisive role to social change. Internet has become the most effective way for information transmission, whose role is network security. How to guarantee network security has become a serious and worrying problem. Biometric identification technology has some advantages including universality, uniqueness, stability and hard to be stolen. Through comparing with the other methods of biometric identification technology, such as fingerprint recognition, palm recognition, facial recognition, signature recognition, iris recognition and retina recognition, gene recognition has advantages of exclusiveness, never change, convenience and a large amount of information, which is thought to be the most important method of biometric identification technology. With the development of modern technology, the fusion of biological technology and information technology has become an inevitable trend. Biometric identification technology will necessarily replace the traditional identification technology and greatly change the life-style of people in the near future.

Dynamic grouping multi-classes face recognition method

Abstract: The paper presents a dynamic grouping multi-class face recognition method, which has knowledge-increasable ability and can solve the problems of large classes face recognition and pattern classes dynamic extension. By adopting multiple classifiers parallel working in the process of training and dynamic grouping recognition, the method can not only speed up calculation and improve the recognition rate but also achieve extension easily and freely. Experimental results prove its reasonableness and feasibility.

Review on offline signature verification methods based on artificial intelligence technique

Abstract: Signature can be used as a biometric is implemented in various systems as well as every signature signed by each person is distinct at the same time. It is very important to have a computerized signature verification system. In case of offline signature verification system dynamic features are not included obviously, but we can use a signature as an image and apply image processing techniques to make an effective offline signature verification system. Author wants to illustrate two techniques reviewed by him on Offline signature Verification. Those techniques are mixed of Energy with Angle and Energy with Chain Code.

Dynamic signature recognition for automatic student authentication

Abstract: Handwritten signatures are one of the most socially accepted biometric traits. Signatures are commonly used in financial and legal agreements since more than a century. In education, signatures are used for attendance control, either to lectures or exams, but not for (automatic) authentication. With the rapid deployment of dynamic signature recognition, this technology is ready to be used for student authentication. Also, the use of this technology can be extended to different administrative services within the education system, in order to add a higher security level to the traditional procedures of authentication (e.g., visually checking the face and/or signature on the person identity card). Nowadays, signatures can be easily captured by means of electronic devices (e.g. pen tablets, PDAs, grip pens, smartphones, etc.). For this reason, the popularity of this biometric trait is rapidly increasing in the last few years. Even more, signatures can be made using the finger as the writing tool on smartphones. In this paper, we analyse two scenarios for student authentication using their signatures: i) an office scenario with a high quality pen tablet specifically designed to acquire signatures (i.e., Wacom device), and ii) a mobile scenario where users sign on their smartphones with the finger improving this way the usability. For this experimental study we make use of e-BioSign database, which was captured using various modern pen tablet devices and smartphones. The database contains signatures from 70 users including students and educators, captured in two sessions in different days. The experiments on automatic authentication using dynamic signatures are conducted considering two different types of forgeries, namely: i) random forgeries (the case where an impostor uses his own signature claiming to be another person), and ii) skilled forgeries (where impostors imitate the signature of other persons).