This document discusses developing an algorithm to automatically detect errors in real-time using the Kinect 2 sensor. It aims to reduce costs, automatically attribute errors, increase throughput and data quality, and improve matching rates. The purpose is to create a program using Kinect 2 to detect errors in real-time when integrated with an AOptix iris camera, with the goal of proving this concept. Potential limitations include infrared interference between the devices and lag between log writing and reading.

1. HBSI AUTOMATION
Using the Kinect

2. CHAPTER 1

3. •There is no way currently to automatically
detect HBSI errors in real-time using the Kinect
2
STATEMENT OF THE PROBLEM
8.8.6

4. •Can an algorithm be developed to
automatically detect HBSI errors in real-time
using the Kinect 2
RESEARCH QUESTION/HYPOTHESIS
8.8.7

5. • Reduce cost for the vendor
• Automatically attribute errors
• Increase throughput time
• Better quality of data
• Better match rates
• Reduction in habituation and the need for training
• Increase center efficiency in providing guidance on
errors
SIGNIFICANCE OF THE PROBLEM
8.8.8

6. •Develop a program that uses the Kinect 2 to
automatically detect HBSI errors in real-time
•To show the proof of concept, I’ll integrate with
the AOptix iris camera
STATEMENT OF PURPOSE/SCOPE
8.8.9

7. •Critical points will be finished
•Microsoft Kinect software is appropriate
•Kinect 2 is a reliable tool for meaurement
ASSUMPTIONS
8.8.10

8. •Potential infrared interference between AOptix
and Kinect 2
•Potential lag between AOptix logs being written
and being read by the program
LIMITATIONS
8.8.11

9. •HBSI errors will be pre-defined
•Only for iris modality
•Not detecting quality
DELIMITATIONS
8.8.12

10. CHAPTER 2

11. LITERATURE REVIEW

12. CHAPTER 3

13. CHAPTER 4

14. CHAPTER 5