Signature recognition

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

Stride Towards Proposing Multi-Modal Biometric Authentication for Online Exam

Abstract: Biometric authentication has been getting widespread attention over the past decade with growing demands in automated secured personal identification and has been employed in diverse fields. It ensures actual presence of biometric entity of a person in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem. Also in the previous work they use face and dress color as hard and soft biometric traits. The major drawback of the existing continuous authentication system is, it is able to successfully authenticate the user continuously with high tolerance to the user posture. So, to overcome this drawback and improve the systems robustness against illumination changes and cluttered background, in this paper we use additional biometric traits which are mole, ornament details and face dimensions in addition to the dress color and face color. Also, we extend it to the online exam application. That is, continuously monitoring of a person in an online exam is proposed employing hard biometric like facial recognition and soft biometrics. Modified PCA (Principal Component Analysis) is employed here for the facial recognition part. Both the hard biometric (face) and soft biometrics is fused with the help of optimization algorithm based similarity technique. Finally the authentication is performed and evaluated using standard evaluation metrics. The technique is implemented in MATLAB and will be compared to prominent existing techniques.

Finger-vein recognition with modified binary tree model

Abstract: Finger-vein recognition is an increasingly promising biometric identification technology in terms of its high identification accuracy and prominent security performance. The main challenge faced by finger-vein recognition is the low recognition performance caused by segmentation error and local difference. To tackle this challenge, a finger-vein recognition method with modified binary tree (MBT) model is proposed in this paper. MBT model is used to describe the relationship and spatial structure of vein branches quantitatively. Based on the MBT model, four stages including rough selection, model correction, segment matching, and comprehensive judgment are presented to achieve a robust matching for finger-vein. Experiments demonstrate that the proposed method can boost the performance of finger-vein recognition that is degraded by segmentation error and local difference. While maintaining low complexity, the proposed method achieves 0.12 % equal error rate in the introduced dataset with 8,100 finger-vein images from 150 participants, which outperforms the state-of-the-art methods.

Robustness to expression variations in fractal-based face recognition

Abstract: Face recognition has developed into a major research area in pattern recognition and computer vision. Face recognition is different from classical pattern recognition problems such as character recognition. In classical pattern recognition, there are relatively few classes, and many samples per class. With many samples per class, algorithms can classify samples not previously seen by interpolating among the training samples. On the other hand, in face recognition, there are many individuals (classes), and only a few images (samples) per person, and algorithms must recognize faces by extrapolating from the training samples. In numerous applications there can be only one training sample (image) of each person (for real time applications). In this paper, we present an approach for expression-invariant face recognition based on fractal features.

Assessing biometric sample quality using wavelets and a boosted classifier

Abstract: A biometric sample training device, a biometric sample quality assessment device, a biometric fusion recognition device, an integrated biometric fusion recognition system and example processes in which each may be used are described. Wavelets and a boosted classifier are used to assess the quality of biometric samples, such as facial images. The described biometric sample quality assessment approach provides accurate and reliable quality assessment values that are robust to various degradation factors, e.g., such as pose, illumination, and lighting in facial image biometric samples. The quality assessment values allow biometric samples of different sample types to be combined to support complex recognition techniques used by, for example, biometric fusion devices, resulting in improved accuracy and robustness in both biometric authentication and biometric recognition.

A Proposed Framework for Enhancing Security in Fingerprint and Finger-Vein Multimodal Biometric Recognition

Abstract: Biometrics is the way of expedient and scrutinizing the physical attributes of a person. An assortment of inevitable shortcomings has been faced by unimodal biometric recognition like noisy biometric data, Limited discriminability, Upper bound in performance and Lack of permanence, consequence degradation of accuracy and performance of the system. Multimodal biometrics consolidates the two or more biometric traits into a single detection. Problems transpired in unimodal recognition can be alleviated by using multimodal biometric systems that fuse evidence from scores of multiple biometric systems and characteristically provide better recognition as compared to unimodal biometric systems. Biometric authentications exploit inimitable combination of measurable physical Characteristics-fingerprint, finger vein features, voice print, iris of the eye, and so on-that cannot be willingly imitated or forged by others. This paper proffers the score level of fusion with feature extraction that can be espoused to consolidate the scores attained by fingerprint and vein and new approach fusion with alignment that are credible and the integration strategic that can be espoused to overlapped the features attained by fingerprint and vein. Fusion techniques include processing biometric modalities successively until an adequate match is obtained.