Topic
Signature recognition
About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.
Papers published on a yearly basis
Papers
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03 Apr 1990
TL;DR: Several attempts to improve recognition accuracy with the use of supervised clustering techniques are described, which improved the phonetic recognition capability of the vector quantization, but the overall word and sentence recognition accuracy did not improve.
Abstract: Several attempts to improve recognition accuracy with the use of supervised clustering techniques are described. These techniques modify the distance metric and/or the clustering procedure in a discrete hidden Markov model recognition system in an attempt to improve phonetic modeling. Three techniques considered are linear discriminant analysis, a hierarchical supervised vector quantization technique, and Kohonen's LVQ2 technique. All experiments were performed on the DARPA resource management speech corpus using the BBN BYBLOS system. Even though the techniques improved the phonetic recognition capability of the vector quantization, the overall word and sentence recognition accuracy did not improve. >
43 citations
Proceedings Article•
01 Jan 1998
TL;DR: Modifications to a probabilistic segmentation algorithm are investigated to achieve a real-time, and pipelined capability for the authors' segment-based speech recognizer and produce 30% fewer segments on a word recognition task in a weather information domain.
Abstract: In this work, we investigate modifications to a probabilistic segmentation algorithm to achieve a real-time, and pipelined capability for our segment-based speech recognizer [4]. The existing algorithm used a Viterbi and backwards A search to hypothesize phonetic segments [2]. We were able to reduce the computational requirements of this algorithm by reducing the effective search space to acoustic landmarks, and were able to achieve pipelined capability by executing theA search in blocks defined by reliably detected phonetic boundaries. The new algorithm produces 30% fewer segments, and improves TIMIT phonetic recognition performance by 2.4% over an acoustic segmentation baseline. We were also able to produce 30% fewer segments on a word recognition task in a weather information domain [11].
42 citations
19 May 2015
TL;DR: It is shown, that by constructing a binary tree data structure of Bloom filters extracted from binary iris biometric templates (iris-codes) the search space can be reduced to O(logN).
Abstract: Conventional biometric identification systems require exhaustive 1 ∶ N comparisons in order to identify biometric probes, i.e. comparison time frequently dominates the overall computational workload. Biometric database indexing represents a challenging task since biometric data is fuzzy and does not exhibit any natural sorting order. In this paper we present a preliminary study on the feasibility of applying Bloom filters for the purpose of iris biometric database indexing. It is shown, that by constructing a binary tree data structure of Bloom filters extracted from binary iris biometric templates (iris-codes) the search space can be reduced to O(logN). In experiments, which are carried out on a database of N = 256 classes, biometric performance (accuracy) is maintained for different conventional identification systems. Further, perspectives on how to employ the proposed scheme on large-scale databases are given.
42 citations
Proceedings Article•
01 Jan 1995
TL;DR: An approach to cluster the training data for automatic speech recognition (ASR) using a relative-entropy based distance metric between training data clusters is presented.
Abstract: We present an approach to cluster the training data for automatic speech recognition (ASR). A relative-entropy based distance metric between training data clusters is deened. This metric is used to hierarchically cluster the training data. The metric can also be used to select the closest training data clusters given a small amount of data from the test speaker. The selected clusters are then used to estimate a set of hidden Markov models (HMMs) for recognizing the speech from the test speaker. We present preliminary experimental results of the clustering algorithm and its application to ASR.
42 citations
27 Oct 2004
TL;DR: This work overviews biometric authentication and presents a system for on-line signature verification, approaching the problem as a two-class pattern recognition problem, using standard pattern classification techniques.
Abstract: We overview biometric authentication and present a system for on-line signature verification, approaching the problem as a two-class pattern recognition problem. During enrollment, reference signatures are collected from each registered user and cross aligned to extract statistics about that user’s signature. A test signature’s authenticity is established by first aligning it with each reference signature for the claimed user. The signature is then classified as genuine or forgery, according to the alignment scores which are normalized by reference statistics, using standard pattern classification techniques. We experimented with the Bayes classifier on the original data, as well as a linear classifier used in conjunction with Principal Component Analysis (PCA). The classifier using PCA resulted in a 1.4% error rate for a data set of 94 people and 495 signatures (genuine signatures and skilled forgeries).
42 citations