Signature recognition

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

Hidden Markov model with nonstationary states

Abstract: In this paper we explore the nonstationarity of Markov model and we propose a nonstationary Hidden Markov Model (NSHMM) which is defined with a set of dynamic transitions probability parameters A(t)={a ij (t)} that depend on the time t already spent in state i. When compared to traditional models, this NSHMM is defined as generalization of the DHMM. The model was applied to on line recognition of handwritten arabie characters. The characters are represented by a radial sequence which is independent of translation, orientation and homothetie. The complete symbol-generation procedure includes sampling, size normalization and quantization phases. In the training, the model parameters are estimated with Baum-welch algorithm from a set of characters. Experiments have been conducted, and a good classification score has been obtained. The discrimination between characters models has been improved. Experiments showed that this approach can better capture the dynamic nature of arabie script.

Vector signature for face recognition

Abstract: In this paper, we proposed a vector signature scheme for face recognition. Using the signature, both the database size and communication bandwidth can be reduced. And the privacy of the face image is also improved. Some experimental implementation shows the potential of the new proposal.

Using bilinear maps and its applications for one forward- secure signature scheme

Abstract: In this paper, a method for the automatic handwritten signature verification (AHSV) is described. The method relies on global features that summarize different aspects of signature shape and dynamics of signature production. For designing the algorithm, we have tried to detect the signature without paying any attention to the thickness and size of it. The results have shown that the correctness of our algorithm detecting the signature is more acceptable. In this method, first the signature is pre-processed and the noise of sample signature is removed. Then, the signature is analyzed and specification of it is extracted and saved in a string for the comparison. At the end, using adapted version of the dynamic time warping algorithm, signature is classified as an original or a forgery one

Random access detection device and method in TD-SCDMA system

Abstract: The invention relates to a random access detection device in a TD-SCDMA system, which comprises a GpUp data buffering unit, a matched filter unit, a signature recognition unit, a rapid physical accesschannel generation unit and a rapid physical access channel coding unit. The invention also provides a random access detection method on the basis of the device. Data received by a receiver is buffer ed in the GpUp data buffering unit, and the matched filter unit carries out shift-related and matched filter energy value operation to obtain a maximum energy value and a corresponding position sequence number; the signature recognition unit receives a valid signature, and generates signature recognition information; and the rapid physical access channel generation unit processes the signature recognition information to form rapid physical access channel information, and the rapid physical access channel coding unit codes the rapid physical access channel information. The invention has clear structure and relatively smaller complexity, greatly reduces the realization cost of the existing engineering, and enhances the operational efficiency.

Model For Pattern Recognition

Abstract: In this paper, a model based upon the automaton theory and used for pattern recognition is introduced. The main purpose of this model is to evaluate the performance of a pattern recognition computer and an algorithm for pattern recognition. This model abstracts main frame from new generation pattern recognition computers and is defined as follows: M=(P1, W, B, P, P1' ,S), where p1 is a set of input patterns; W is a set of strings representing a program or explanations for processing; B is a knowledge base storing rules and feature functions of puzzy sets; P represents computer systems for pattern recognition; P1'is a set of output patterns; S is the processing results. By using this model, the performance of a pattern recognition computer and an algorithm for pattern recognition can be easily evaluated.