Signature recognition

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

Enhancements on a signature recognition problem

Abstract: This paper presents an enhanced method of partitioning a dataset into clusters when dealing with the handwritten signature recognition problem. The goal of the present system is improving the performance of two previously developed systems. In the first version of our system we dealt with data extraction from signature images and obtained a recognition rate of 91.04% using the Naive Bayes classifier and the feature selection method. In the second version of our system we performed a hierarchical partitioning of the dataset into clusters in order to obtain a faster and better classification and then we applied the Naive Bayes classifier in order to determine the recognition rate. The best results were reported when partitioning the data into 7 and 8 clusters and the mean accuracy obtained was 91%. In the current version of our system, we identified the most appropriate model for each cluster by selecting the best performance obtained after applying 7 different classifiers on the clusters. In order to improve the recognition rate and reduce the time required to build a model, we applied the feature selection method on each of the clusters with the previously determined classifiers. We obtained an increased accuracy with 1.62%on a dataset with 14 instances/class and an increment of 3.24% on a dataset with 20 instances/class.

A Nonparametric Functional Method for Signature Recognition

Abstract: We propose to use nonparametric functional data analysis techniques within the framework of a signature recognition system. Regarding the signature as a random function from \( \mathbb{R} {\rm(time \,domain)\,to}\, \mathbb{R}^2\) (position (x,y) of the pen), we tackle the problem as a genuine nonparametric functional classification problem, in contrast to currently used biometrical approaches. A simulation study on a real data set shows good results.

Text-independent speaker recognition for Ambient Intelligence applications by using Information Set Features

Abstract: Biometric systems are enabling technologies for a wide set of applications in Ambient Intelligence (AmI) environments. In this context, speaker recognition techniques are of paramount importance due to their high user acceptance and low required cooperation. Typical applications of biometric recognition in AmI environments are identification techniques designed to recognize individuals in small datasets. Biometric recognition methods are frequently deployed on embedded hardware and therefore need to be optimized in terms of computational time as well as used memory. This paper presents a text-independent speaker recognition method particularly suitable for identification in AmI environments. The proposed method first computes the Mel Frequency Cepstral Coefficients (MFCC) and then creates Information Set Features (ISF) by applying a fuzzy logic approach. Finally, it estimates the user's identity by using a hierarchical classification technique based on computational intelligence. We evaluated the performance of the speaker recognition method using signals belonging to the NIST-2003 switchboard speaker database. The achieved results showed that the proposed method reduced the size of the template with respect to traditional approaches based on Gaussian Mixture Models (GMM) and achieved better identification accuracy.

Applying fusion in thermal face recognition

Abstract: Face recognition based on thermal images has minor importance in comparison to visible light spectrum recognition. Nevertheless, in the applications such as liveness detection or fever scan, the thermal face recognition is used as a stand-alone module, or as a part of a multi-modal biometric system. This paper investigates the combinations of many methods, used for thermal face recognition, and introduces some new and modified algorithms, which have not been used in this area yet. Moreover, we show that the choice of the best method is always limited to a certain database (input data). In order to address this problem, the multi-algorithmic biometric fusion based on the logistic regression is deployed.