Signature recognition

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

Multimodal biometric template authentication of finger vein and signature using visual cryptography

Abstract: In this paper personal verification method using finger-vein and signature is presented. Among many authentication systems finger-vein is promising as the foolproof method of automatic personal identification. Finger-vein and signature image is pre-processed and features are extracted using cross number concept and principle compound analysis. Fusion technique is used to fuse the finger vein and signature images. Then the visual cryptographic scheme is applied for the biometric template to generate the shares. The shares are stored in a separate database, and then the biometric image is revealed only when both the shares are simultaneously available. At the same time, the individual image does not reveal the identity of the biometric image. The proposed work is evaluated with evaluation metrics FAR, FRR and accuracy.

The reduction of memory and the improvement of recognition rate for HMM on-line handwriting recognition

Abstract: The purpose of this project is two fold. The first purpose is to reduce the memory size of our previous handwriting recognition algorithm based on an HMM using self-organizing map (SOM) density tying. The second is to improve recognition capability by incorporating additional information. SOM density tying reduced the dictionary size to 1/7 of the original size, with a recognition rate of 90.45%, only slightly less than the original recognition rate of 91.51%. Our additional feature increased recognition capability to 91.34%.

A Unifying Approach to ECG Biometric Recognition Using the Wavelet Transform

Abstract: Biometric recognition systems use measures from the body itself to determine the identity of an individual. The electrocardiogram (ECG) has been increasingly used as a biometric measure for person identification, as it is an easily measurable characteristic of all individuals. Our method for ECG acquisition follows an off-the-person approach, using a single ECG lead with non-gelled electrodes placed at the hands. However, this signal is noisier than typical ECG signals acquired on the chest, making subsequent processing more difficult. Therefore, we investigate the applicability of the Wavelet Transform (WT), which decomposes a signal into a time-scale representation according to a given mother wavelet. We use this representation to both segment the R wave of the ECG signal, and as the features for the classification step, defining an appropriate distance measure. We test this framework with real data, using various mother wavelets. Our experimental results show the potential of this framework, and that the best mother wavelet for the evaluated context is the rbio5.5.

Design of multimodal biometrics system based on feature level fusion

Abstract: Multimodal system aims to fuse two or more biometrics traits of an individual to achieve improvement in FAR and FRR of biometrics system which in turn increases accuracy of system. In this paper we have proposed biometrics system based on biometrics traits face and signature. The performances of face and signature recognition can be enhanced using a proposed feature selection method to select an optimal subset of features. Signature is very important human characteristics which is required in all financial transaction for human identification. In case of financial transaction correct recognition is necessary otherwise it can lead to fraudulent activities. Face is most commonly acceptable and popular biometrics. Proposed algorithm fuses wavelet based features of face and signature. Wavelet based feature fusion method also gave very promising results. Hamming distance classifier is used to take decision whether person is genuine or imposter. Our experiments show that the proposed algorithm can achieve higher classification accuracy than offline signature and face based identification system. We have achieved false accept rate of 5.99% and 3% for multibiometrics system for ORL databases combined with Caltech and Ucoer real signature database resp.

A novel framework for automatic 3D face recognition using quality assessment

Abstract: The quality of biometric samples plays an important role in biometric authentication systems because it has a direct impact on verification or identification performance In this paper, we present a novel 3D face recognition system which performs quality assessment on input images prior to recognition More specifically, a reject option is provided to allow the system operator to eliminate the incoming images of poor quality, eg failure acquisition of 3D image, exaggerated facial expressions, etc Furthermore, an automated approach for preprocessing is presented to reduce the number of failure cases in that stage The experimental results show that the 3D face recognition performance is significantly improved by taking the quality of 3D facial images into account The proposed system achieves the verification rate of 9709% at the False Acceptance Rate (FAR) of 01% on the FRGC v20 data set