Biometric Recognition Methods Research Articles

Extraction and application of dynamic pupillometry features for biometric authentication

Iris recognition is considered one of the most secure biometric recognition methods. However, even this biometric trait can be frauded in some cases. In other cases, the quality of the captured image can affect the system recognition performance. This paper proposes a multimodal biometric authentication system that combines the use of dynamic features from the Pupillary Light Reflex (PLR) and the static features from the iris pattern for a better performance. A dynamic pupillometer device has been developed and a prototype system for features extraction and classification has been implemented. A pupillometric database has been created using data from 90 volunteers, and tests of the biometric system have lead to experimental of 0% EER.

Biometric watermarks based on face recognition methods for authentication of digital images

AbstractBecause making digital images secure runs into the substantial challenge of owner authentication, many security schemes based on cryptography, steganography and watermarking technology include biometric recognition methods. To follow on these studies, this paper describes a combination of facial images with watermarking technology to automatically authenticate digital images owners/users. In the proposed methodology, biometric face recognition methods such as principal component analysis and eigenfeature regularization and extraction produce vectorial representations of facial images. These vectors are used as copyright watermarks, in a few common watermarking schemes, and are tested for identification purposes after they are extracted. Initially, watermarking algorithms are studied with some arbitrary cover image, and also the most robust algorithm is tested for different cover images of particular subjects. The strength of this paper is finding relationships between the original and extracted biometric data using neural networks instead of the most common, simple measures such as correlation coefficients or distance metrics. The NN subject identification is performed directly, as there is no need to reconstruct facial images after the watermarks are extracted, compute templates for particular subjects, or seek a suitable distance metric. What is more, the presented study includes a performance comparison of two machine learning methods, frequently used for face recognition, and of a few popular watermarking algorithms. Very promising identification results were obtained in many considered experiments, even those involving attacks on watermarked images. Copyright © 2014 John Wiley & Sons, Ltd.

부인봉쇄 성질을 갖는 바이오메트릭 서명 위임 기법

Abstract In a biometric signature scheme, a user’s biometric key is used to sign the document. It also requires the user be authenticated with biometric recognition method, prior to signing the document. Because the biometric recognition is launched every time the signature session started, it is not suitable for electronic commerce applications such as shopping malls where large number of documents to sign are required. Therefore, to commercialize biometric based signature schemes, the new proxy signature scheme is needed to ease the burden of the signer. In the proxy signature scheme, the signer can delegate signing activities to trustful third parties. In this study, the biometric based signature delegation method is proposed. The proposed scheme is suitable for applications where a lot of signing are required. It is consisted of biometric key generation, PKI based mutual authentication, signature generation and verification protocols. Key Words : Biometric Signature, Proxy Signature, PKI, Undeniable Property, Large Scale Authentication

A multi-modal approach for high-dimensional feature recognition

Over the past few decades, biometric recognition firmly established itself as one of the areas of tremendous potential to make scientific discovery and to advance state-of-the- art research in security domain. Hardly, there is a single area of IT left untouched by increased vulnerabilities, on-line scams, e-fraud, illegal activities, and event pranks in virtual worlds. In parallel with biometric development, which went from focus on single biometric recognition methods to multi-modal information fusion, another rising area of research is virtual world's security and avatar recognition. This article explores links between multi-biometric system architecture and virtual worlds face recognition, and proposes methodology which can be of benefit for both applications.

Two Level Centre of Gravity Computation An Important Parameter for Offline Signature Recognition

Dean (R&D) RCET, Bhilai, (C.G) India, ABSTRACTpaper proposed feature extraction method based on centre of gravity for offline signature. Similar to other biometric measures, signatures have inherent variability and so pose a difficult recognition problem. Signature probably is one of the oldest biometric recognition methods, with high legal acceptance Signature has been a distinguishing feature for person identification through ages. Even today an increasing number of transactions, especially financial, are being authorized via signatures. In this paper, signature is preprocessed through binarization, cutting edges and thinning which provides more accurate platform for feature extraction methods. We have computed centre of gravity in two level by considering centre of gravity of all the characters separately instead of taking one common centre of gravity for entire signature and finally we would be able to built a system for signature recognition by taking mean values of all the centre of gravity values of various characters present in the signature.

Signature recognition state-of-the-art

A summarization of one of the most successful behavioral biometric recognition methods: signature recognition. Probably this is one of the oldest biometric recognition methods, with high legal acceptance. Technological advances have made possible new perspectives for signature recognition, by means of capturing devices which provide more than the simple signature image: pressure, acceleration, etc., making it even more difficult to forge a signature.