Matching Error Rate Research Articles

Map-matching Algorithm Based on Junction Judgment Domain Model

We made a thorough research of map matching for intersections, and proposed a new junction judgment domain model. The new model includes the width of the junction-connected sections, GPS accuracy, and the road network data accuracy. It is more realistic and better operability. On the basis of this model, we designed a new map-matching algorithm combined with the motion characteristics of a vehicle in the period from moving into a junction judgment domain to out of it. The experimental results showed that the new map matching algorithm was able to effectively reduce the junction matching error rates and improve the stability and user experience of navigation system.

An Optimized Wavelength Band Selection for Heavily Pigmented Iris Recognition

Commercial iris recognition systems usually acquire images of the eye in 850-nm band of the electromagnetic spectrum. In this work, the heavily pigmented iris images are captured at 12 wavelengths, from 420 to 940 nm. The purpose is to find the most suitable wavelength band for the heavily pigmented iris recognition. A multispectral acquisition system is first designed for imaging the iris at narrow spectral bands in the range of 420-940 nm. Next, a set of 200 human black irises which correspond to the right and left eyes of 100 different subjects are acquired for an analysis. Finally, the most suitable wavelength for heavily pigmented iris recognition is found based on two approaches: 1) the quality assurance of texture; 2) matching performance-equal error rate (EER) and false rejection rate (FRR). This result is supported by visual observations of magnified detailed local iris texture information. The experimental results suggest that there exists a most suitable wavelength band for heavily pigmented iris recognition when using a single band of wavelength as illumination.

Increased orienting to unexpected action outcomes in schizophrenia

Although some recent research has indicated reduced performance monitoring in patients with schizophrenia, the literature on this topic shows some remarkable inconsistencies. While most studies suggest diminished error signals following error responses, some studies reported normal post-error slowing, while others reported reduced post-error slowing. Here we review these studies and highlight the most important discrepancies. Furthermore, we argue that overall error rates are a mostly neglected issue that can at least partly explain these discrepancies. It has been reported previously that post-error slowing depends on the error rates. Participants or patients that make more errors are likely to show decreased post-error slowing. Therefore, when a group of patients is compared to a group of controls, it is extremely important to match error rates. For this purpose, we developed a procedure where we matched individuals' error rates. In a task where subjects had to press a response key corresponding to one of four colors we manipulated the difficulty on an individual basis by varying the discriminability between the colors. Schizophrenic patients and a group of controls were tested with this procedure showing that differences in accuracy disappear. Interestingly, we can see that in patients, the color values that were needed to reach similar levels of accuracy correlate with the Positive and Negative Syndrome Scale (PANSS) scale, with higher PANSS requiring more color. Most important, we found that schizophrenic patients have increased rather than decreased post-error slowing when the inter-trial interval (ITI) is short. This result can be interpreted within the framework of the orienting account, as it has been demonstrated previously that schizophrenic patients show increased distractibility.

Key Factors Affecting the Accuracy of Reidentification of Trucks over Long Distances Based on Axle Measurement Data

Vehicle reidentification methods can be used to anonymously match vehicles crossing two locations based on vehicle attribute data. This paper investigates key factors that affect the accuracy of vehicle reidentification algorithms. The analyses are performed with reidentification algorithms to match commercial vehicles that cross upstream and downstream pairs of weigh-in-motion (WIM) sites that are separated by long distances, ranging from 70 to 214 mi. The data to support this research come from 17 fixed WIM sites in Oregon. Data from 14 pairs of WIM sites are used to evaluate how various factors affect matching accuracy; factors include the distance between two sites, travel time variability, truck volumes, and sensor accuracy or consistency of measurements. After the vehicle reidentification algorithm is run for each of these 14 pairs of sites, the matching error rates are reported. The results from the testing data sets show a large variation in terms of accuracy. Sensor accuracy and volumes have the greatest impacts on matching accuracy; distance alone does not have a significant effect.

An Improved Multimodal Palm Vein Recognition System Using Shape and Texture Features

1 Abstract—Multimodal analysis of palm vein for biometric authentication has been carried out and presented in this paper. The proposed system is based on parallel mode multiple feature analysis and multilevel fusion. The directional information of the palm vein has been considered for better analysis. The developed multimodal palm vein recognition system has very low value of matching error rate and false acceptance or rejection rate. Index Terms—Palm vein recognition, Multimodal Biometrics, Feature subset selection, ASFFS, FAR, FRR

A Parametric Correlation Framework for the Statistical Evaluation and Estimation of Biometric-Based Classification Performance in a Single Environment

In this paper, we propose parametric correlation models for the assessment of biometric classification error rates. Correctly specified correlations are integral to variance estimation and the corresponding inferential quantities which depend upon these estimates. We present methodology here for false match and false nonmatch error rates for a single environment. This paper generalizes other work that has previously appeared in the bioauthentication literature. Since symmetric- and asymmetric-matching algorithms are used in practice, we present a general correlation structure for both types of algorithms. Along with the correlation structure, we describe estimators for the parameters in these models. The correlation structure described here for binary decision data is then used to derive explicit confidence intervals and sample-size calculations for the estimation of false match and false nonmatch error rates. We then apply the correlation structure described herein to two match scores databases to illustrate our approach. A discussion of the utility and consequences of this correlation structure are also provided.

High-speed parallel very large scale integration architecture for global stereo matching

Although stereo matching algorithms based on belief propagation (BP) tend to show excellent matching performance, their huge computational complexity has been the major barrier to real-time applications. In this light, we propose a parallel very large scale integration (VLSI) architecture for BP computation, which has only simple integer operations and shows low matching error rate for the Middlebury database.

Statistical Models for Assessing the Individuality of Fingerprints

Following the Daubert ruling in 1993, forensic evidence based on fingerprints was first challenged in the 1999 case of the U.S. versus Byron C. Mitchell and, subsequently, in 20 other cases involving fingerprint evidence. The main concern with the admissibility of fingerprint evidence is the problem of individualization, namely, that the fundamental premise for asserting the uniqueness of fingerprints has not been objectively tested and matching error rates are unknown. In order to assess the error rates, we require quantifying the variability of fingerprint features, namely, minutiae in the target population. A family of finite mixture models has been developed in this paper to represent the distribution of minutiae in fingerprint images, including minutiae clustering tendencies and dependencies in different regions of the fingerprint image domain. A mathematical model that computes the probability of a random correspondence (PRC) is derived based on the mixture models. A PRC of 2.25 times10-6 corresponding to 12 minutiae matches was computed for the NIST4 Special Database, when the numbers of query and template minutiae both equal 46. This is also the estimate of the PRC for a target population with a similar composition as that of NIST4.

Match Bias in Wage Gap Estimates Due to Earnings Imputation

About 30% of workers in the Current Population Survey have earnings imputed. Wage gap estimates are biased toward zero when the attribute being studied (e.g., union status) is not a criterion used to match donors to nonrespondents. An expression for “match bias” is derived in which attenuation equals the sum of match error rates. Attenuation can be approximated by the proportion with imputed earnings. Union wage gap estimates with match bias removed are presented for 1973–2001. Estimates for recent years are biased downward 5 percentage points. Bias in gap estimates accompanying other non–match criteria (public sector, industry, etc.) is examined.

SPEED, ACCURACY, AND FLEXIBILITY TRADE-OFFS IN ON-LINE CHARACTER RECOGNITION

An on-line character recognizer was improved by making trade-offs on computation speed, recognition accuracy, and flexibility. Firstly, the error rate was halved by increasing computation precision at the expense of speed, achieving a character recognition accuracy of 97.3 percent. Secondly, without loss of accuracy, computation speed was increased by an order of magnitude over the original speed, to 85 characters/second on the IBM System 370/Model 3081. This was done by using a fast linear match to narrow the character choices for a slow but accurate elastic (non-linear) match. Some loss in flexibility resulted, however, because the linear match requires the same number of handwritten strokes for input and prototype characters. Also, the error rate of elastic matching was found to be about half that of linear matching.

Compatible High and Low Voltage CMOS Devices Using SIMOX Technology

New type high and low voltage CMOS buried channel devices are described, which are applied to a current mirror and an operational amplifier using SIMOX technology. In the MOS current mirrors, a 0.5% matching error rate was obtained in a 10 µA drain current without any compensation circuits. In the two stage operational amplifier, an open loop voltage gain of 60 dB was obtained with a ±5 V supply.