The Experts below are selected from a list of 10992 Experts worldwide ranked by ideXlab platform

Anil K Jain - One of the best experts on this subject based on the ideXlab platform.

  • biometric template selection and update a case study in fingerprints
    Pattern Recognition, 2004
    Co-Authors: Umut Uludag, Arun Ross, Anil K Jain
    Abstract:

    A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a Claimed Identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques.

  • biometric template selection and update a case study in fingerprints
    Pattern Recognition, 2004
    Co-Authors: Umut Uludag, Arun Ross, Anil K Jain
    Abstract:

    A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a Claimed Identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques.

  • multimedia document authentication using on line signatures as watermarks
    Conference on Security Steganography and Watermarking of Multimedia Contents, 2004
    Co-Authors: Anoop M Namboodiri, Anil K Jain
    Abstract:

    Authentication of digital documents is an important concern as digital documents are replacing the traditional paper-based documents for offcial and legal purposes. This is especially true in the case of documents that are exchanged over the Internet, which could be accessed and modified by intruders. The most popular methods used for authentication of digital documents are public key encryption-based authentication and digital watermarking. Traditional watermarking techniques embed a pre-determined character string, such as the company logo, in a document. We propose a fragile watermarking system, which uses an on-line signature of the author as the watermark in a document. The embedding of a biometric characteristic such as signature in a document enables us to verify the Identity of the author using a set of reference signatures, in addition to ascertaining the document integrity. The receiver of the document reconstructs the signature used to watermark the document, which is then used to verify the author's Claimed Identity. The paper presents a signature encoding scheme, which facilitates reconstruction by the receiver, while reducing the chances of collusion attacks.

Umut Uludag - One of the best experts on this subject based on the ideXlab platform.

  • biometric template selection and update a case study in fingerprints
    Pattern Recognition, 2004
    Co-Authors: Umut Uludag, Arun Ross, Anil K Jain
    Abstract:

    A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a Claimed Identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques.

  • biometric template selection and update a case study in fingerprints
    Pattern Recognition, 2004
    Co-Authors: Umut Uludag, Arun Ross, Anil K Jain
    Abstract:

    A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a Claimed Identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques.

Arun Ross - One of the best experts on this subject based on the ideXlab platform.

  • biometric template selection and update a case study in fingerprints
    Pattern Recognition, 2004
    Co-Authors: Umut Uludag, Arun Ross, Anil K Jain
    Abstract:

    A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a Claimed Identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques.

  • biometric template selection and update a case study in fingerprints
    Pattern Recognition, 2004
    Co-Authors: Umut Uludag, Arun Ross, Anil K Jain
    Abstract:

    A biometric authentication system operates by acquiring biometric data from a user and comparing it against the template data stored in a database in order to identify a person or to verify a Claimed Identity. Most systems store multiple templates per user in order to account for variations observed in a person's biometric data. In this paper we propose two methods to perform automatic template selection where the goal is to select prototype fingerprint templates for a finger from a given set of fingerprint impressions. The first method, called DEND, employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method, called MDIST, selects templates that exhibit maximum similarity with the rest of the impressions. Matching results on a database of 50 different fingers, with 200 impressions per finger, indicate that a systematic template selection procedure as presented here results in better performance than random template selection. The proposed methods have also been utilized to perform automatic template update. Experimental results underscore the importance of these techniques.

Yillbyung Lee - One of the best experts on this subject based on the ideXlab platform.

  • biometric authentication system using reduced joint feature vector of iris and face
    Lecture Notes in Computer Science, 2005
    Co-Authors: Byungjun Son, Yillbyung Lee
    Abstract:

    In this paper, we present the biometric authentication system based on the fusion of two user-friendly biometric modalities: Iris and Face. Using one biometric feature can lead to good results, but there is no reliable way to verify the classification. In order to reach robust identification and verification we are combining two different biometric features. we specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from iris and face. And then to obtain Reduced Joint Feature Vector(RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. This system can operate in two modes: to identify a particular person or to verify a person's Claimed Identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

Kotagiri Ramamohanarao - One of the best experts on this subject based on the ideXlab platform.

  • biometric security application for person authentication using retinal vessel feature
    Digital Image Computing: Techniques and Applications, 2013
    Co-Authors: Akter Hussain, Alauddin Bhuiyan, Ajmal Mian, Kotagiri Ramamohanarao
    Abstract:

    Retinal vascular branch and crossover points are unique features for each individual that can be used as a reliable biometric for personal authentication and can be used for information retrieval and security application. In this work, a novel biometric authentication scheme is proposed based on the retinal vascular network features. We apply an automatic technique to detect and identify retinal vascular branch and crossover points. These branch and crossover points are mapped from prominent blood vessels in the image. For this, a novel vessel width measurement method is applied and vessels more than certain widths are selected. Based on these vessel segments their corresponding branch and crossover points are identified. Invariant features are constructed through Geometric Hashing of the detected branch and crossover points. We consider the crossover points for modelling a basis pair and all other points together for locations in the hash table entries. Thus, the models are invariant to rotation, translation and scaling. For each person, the system is trained with the models to accept or reject a Claimed Identity. The initial results show that the proposed method has achieved 100% detection accuracy which is highly potential for reliable person identification.