Signature recognition

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

Optimum vector quantization codebook design for speaker recognition

Abstract: Vector quantization is a useful method that had been applied to diverse fields such as speaker recognition. This paper describes in detail how to get the optimum vector quantization codebook for the use of speaker recognition. Optimization depends on specific criterion or conditions. The optimization discussed here includes three level optimizations. Level one is locally optimization, level two is globally optimization, level three is personally optimization for speaker recognition.

Close-class-set discrimination method for large-class-set pattern recognition using support vector machines

Abstract: In conventional approaches for multi-class pattern recognition using support vector machines, learning involves discrimination of each class against all the other classes. In the close-class-set discrimination method, learning involves discrimination of each class against a subset of classes confusable with it. The effectiveness of the proposed method is demonstrated for 80-class consonant-vowel recognition.

Universum Learning for Semi-Supervised Signature Recognition from Spatio-Temporal Data

Abstract: We present a novel approach towards signature recognition from spatio-temporal data. The data is obtained by recording gyroscope and accelerometer measurements from an embedded pen device. The idea of Universum learning was previously presented by Vapnik and recently popularized in machine learning community. It assumes that the decision boundary of a classifier lies close to data with high uncertainty. The quality of the final classifier strongly depends on a way how to choose the Universum data and also on the representation of original data. In our paper we use a novel approach of Universum learning to classify signature data, also we present our novel idea how to sample the Universum data. At last, we also find more effective representation of the signature data itself compared to the baseline method. These three novelties allow us to outperform previously published results by 4.89% / 5.58%.

Simple signature verification sub-system for identity recognition

Abstract: The paper proposes a simple, low-cost signature verification approach and software that can be employed as one of the components of a more sophisticated personal identification system. The identification is based on the use of local binary pattern features of a signature image. The Support Vector Machine (SVM) model is chosen to classify the authentic signatures from their imitations.

Research on Multimode Biometric Features Recognition System AdoptingNeural Network

Abstract: The small sample issue is a common problem in face recognition system, and multi-modal model has strong generalization ability to solve the problem of small sample, which has already become the most important area of research in pattern recognition, however, the low accuracy and efficiency of the model has become a major challenge. Based on this, this paper proposes a efficient multimode biometric face and fingerprint recognition system based on neural network, which provides more efficient identification though choosing a good feature extraction and recognition algorithms. The Adoption of biometric recognition to authenticate a person's identity has greatly improved operational efficiency and the recognition accuracy in comparison with adoption of password or passphrase. The feasibility and effectiveness of the method in this paper has been verified in multimode biometric system database.