Showing papers on "Feature hashing published in 2002"

Biometric hash based on statistical features of online signatures

Abstract: Presents an approach to generating biometric hash values based on statistical features in online signature signals. Whilst the output of typical online signature verification systems are threshold-based true-false decisions, based on a comparison between test sample signals and sets of reference signals, our system responds to a signature input with a biometric hash vector, which is calculated based on an individual interval matrix. Especially for applications, which require key management strategies, hash values are of great interest, as keys can be derived directly from the hash value, whereas a verification decision can only grant or refuse access to a stored key. Further, our approach does not require storage of templates for reference signatures, thus increases the security of the system. In our prototype implementation, the generated biometric hash values are calculated on a pen-based PDA and used for key generation for a future secure data communication between a PDA and a server by encryption. First tests show that the system is actuality able to generate stable biometric hash values of the users and although the system was exposed to skilled forgeries, no test person was able to reproduce another subject's hash vector.

Method of hash string extraction

Abstract: A method of hash string extraction from biometric information is disclosed. The method comprises the steps of providing (101) a biometric information sample in the form of a fingerprint for example, extracting (102) features from the biometric information sample and encoding (103) the features based on their location within the biometric information sample; and, generating (104) a string of values based on the extracted features and their determined locations. The method further comprises the steps of hashing (105) the string of symbols to produce a plurality of hash values for comparing the plurality of hash values against a stored hash value for identifying a user.

Hashing methods for temporal data

Abstract: External dynamic hashing has been used in traditional database systems as a fast method for answering membership queries. Given a dynamic set S of objects, a membership query asks whether an object with identity k is in (the most current state of) S. This paper addresses the more general problem of temporal hashing. In this setting, changes to the dynamic set are time-stamped and the membership query has a temporal predicate, as in: "Find whether object with identity k was in set S at time t". We present an efficient solution for this problem that takes an ephemeral hashing scheme and makes it partially persistent. Our solution, also termed partially persistent hashing, uses a space that is linear on the total number of changes in the evolution of set S and has a small {O[log/sub B/(n/B)]} query overhead. An experimental comparison of partially persistent hashing with various straightforward approaches (like external linear hashing, the multi-version B-tree and the R*-tree) shows that it provides the faster membership query response time. Partially persistent hashing should be seen as an extension of traditional external dynamic hashing in a temporal environment. It is independent of the ephemeral dynamic hashing scheme used; while this paper concentrates on linear hashing, the methodology applies to other dynamic hashing schemes as well.

Application of MultiLayer Perceptron Network as a one-way hash function

Abstract: In this paper, the applicability of using a multilayer-perceptron (MLP) network as a possible hash algorithm is investigated. The difficulty of recovering an input from an MLP network hashed output is presented. Important features of good hash algorithms such as resistance to birthday attacks and collision free hashing are explored with regard to the MLP network. Possible advantages of using such an arrangement over existing hash algorithms are mentioned.

Pattern hashing - object recognition based on a distributed local appearance model

Abstract: This paper proposes "Pattern Hashing" as a new scheme for object recognition by effectively introducing an appearance-based approach into the framework of a geometric feature-based approach. We compose multiple bases using a combination of arbitrary three interest points in the model object, compute the geometric invariant for similarity transformation for each basis, and apply a hash function to it. Each image patch consists of pixels which are near the basis vector. We divide the model object image into multiple partial image patches, and create various appearances on the hash table as a distributed local appearance model. In the recognition stage, fast model selection is efficiently executed by the hashing technique, and then appearance pattern matching and voting procedure extract the target object in the input image. Through experiment with a face image database, we demonstrate that partly occluded object regions or multiple object positions can indeed be detected by the proposed algorithm.