(Fall 2012) The Effects of User Characteristics on Iris Capture

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International Center for Biometric Research

The purpose of this study was to determine if there was an impact on iris capture error rates based on various demographic groups. The three demographics which we explored in our analysis were gender, age and height. We noticed that gender and age showed significant differences in error rates. However height appeared to have no impact on error rates.

Tecnologia

THE EFFECTS OF USER CHARACTERISTICS ON IRIS CAPTURE
The purpose of this study was to determine if there was an impact on iris capture error rates based on various
demographic groups. The three demographics which we explored in our analysis were gender, age and height. We
noticed that gender and age showed significant differences in error rates. However height appeared to have no
impact on error rates.
Michael Porter, Andrew Strong, Mark Haworth, Michael Brockly, Stephen Elliott
Overview
Subject Pool
Gender Age
Height (in inches)
45
53
Male
Female
34
35
15
15 18-24
25-35
36-50
51-69
We used the Tukey range test to determine if there is
significant statistical correlation between demographic group
characteristics. Based on error rates, characteristics were
assigned to a group (A or B) to denote if they are statistically
different or similar. P values were less than .05 for all tables.
We found there was significant difference in error rates
between men and women overall.
We also determined that older age groups show significantly
more errors. The 51-69 age group showed very high error rates
as compared to the other groups.
We looked at the data broken down by age for each gender.
We noticed that the greatest gap of failure rates occurred in
the youngest age group (18-24). In this group, women failed
significantly more often.
We found that differences in height played no distinguishable
role in the number of errors.
Gender N Mean (Failure Rate) Grouping
F 53 8.679 A
M 45 5.044 B
Age N Mean (Failure Rate) Grouping
51-69 15 12.133 A
36-50 15 8 A B
25-35 35 4.686 B
18-24 34 6.529 B
Gender
(18-24 year olds)
N Mean (Failure
Rate)
Grouping
F 20 9.050 A
M 14 2.929 B
Results
This is an overview of the whole
population in our data set
based on age and gender. It is
clear that age does play a role
in error rates. However a larger
population could produce
different results.
It is clear from this chart that
there is no significant
correlation between height and
failure rates.
Final Thoughts
• Further studies with larger populations are necessary to verify our findings.
• A follow up study could be performed to determine if other factors affect these error rates. These factors
could include weight, environment, or test administrator.
Failure Rate Scatter Plots
35%
39%
26% 61-65
66-71
72-79
69 Subjects with and an
average height of 67.8”

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Victor Rentea

Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Orbitshub

Architecting Cloud Native Applications

WSO2

Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

Finding Java's Hidden Performance Traps @ DevoxxUK 2024

Victor Rentea

Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood

Juan lago vázquez

MINDCTI Revenue Release Quarter One 2024

MIND CTI

Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

(Fall 2012) The Effects of User Characteristics on Iris Capture

1. THE EFFECTS OF USER CHARACTERISTICS ON IRIS CAPTURE The purpose of this study was to determine if there was an impact on iris capture error rates based on various demographic groups. The three demographics which we explored in our analysis were gender, age and height. We noticed that gender and age showed significant differences in error rates. However height appeared to have no impact on error rates. Michael Porter, Andrew Strong, Mark Haworth, Michael Brockly, Stephen Elliott Overview Subject Pool Gender Age Height (in inches) 45 53 Male Female 34 35 15 15 18-24 25-35 36-50 51-69 We used the Tukey range test to determine if there is significant statistical correlation between demographic group characteristics. Based on error rates, characteristics were assigned to a group (A or B) to denote if they are statistically different or similar. P values were less than .05 for all tables. We found there was significant difference in error rates between men and women overall. We also determined that older age groups show significantly more errors. The 51-69 age group showed very high error rates as compared to the other groups. We looked at the data broken down by age for each gender. We noticed that the greatest gap of failure rates occurred in the youngest age group (18-24). In this group, women failed significantly more often. We found that differences in height played no distinguishable role in the number of errors. Gender N Mean (Failure Rate) Grouping F 53 8.679 A M 45 5.044 B Age N Mean (Failure Rate) Grouping 51-69 15 12.133 A 36-50 15 8 A B 25-35 35 4.686 B 18-24 34 6.529 B Gender (18-24 year olds) N Mean (Failure Rate) Grouping F 20 9.050 A M 14 2.929 B Results This is an overview of the whole population in our data set based on age and gender. It is clear that age does play a role in error rates. However a larger population could produce different results. It is clear from this chart that there is no significant correlation between height and failure rates. Final Thoughts • Further studies with larger populations are necessary to verify our findings. • A follow up study could be performed to determine if other factors affect these error rates. These factors could include weight, environment, or test administrator. Failure Rate Scatter Plots 35% 39% 26% 61-65 66-71 72-79 69 Subjects with and an average height of 67.8”

(Fall 2012) The Effects of User Characteristics on Iris Capture

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