science

Multiple Biometric Grand Challenge

Multiple Biometric Grand Challenge is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Multiple Biometric Grand Challenge rather than just read about it. In short: Multiple Biometric Grand Challenge (MBGC) is a biometric project. Its primary goal is to improve performance of face and iris recognition technology on both still and video imagery with a series of challenge problems and evaluation.

Key takeaways

Multiple Biometric Grand Challenge belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Multiple Biometric Grand Challenge to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Multiple Biometric Grand Challenge from memory before moving on to harder problems.

Reference excerpt

Multiple Biometric Grand Challenge (MBGC) is a biometric project. Its primary goal is to improve performance of face and iris recognition technology on both still and video imagery with a series of challenge problems and evaluation.

Background Over the last decade, numerous government and industry organizations have moved or are moving toward deploying automated biometric technologies to provide increased security for their systems and facilities. Six U.S. Government organizations recently sponsored the Face Recognition Grand Challenge (FRGC), Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. Results from the FRGC and FRVT 2006 documented two orders of magnitude improvement in the performance of face recognition under full-frontal, controlled conditions over the last 14 years. For the first time, ICE 2006 provided an independent assessment of multiple iris recognition algorithms on the same data set. However, further advances in these technologies are needed to meet the full range of operational requirements. Many of these requirements focus on biometric samples taken under less than ideal conditions, for example:

Low quality still images High and low quality video imagery Face and iris images taken under varying illumination conditions Off-angle or occluded images Building on the challenge problem and evaluation paradigm of FRGC, FRVT 2006, ICE 2005 and ICE 2006, the Multiple Biometric Grand Challenge (MBGC) will address these problem areas.

Overview The primary goal of the MBGC is to investigate, test and improve performance of face and iris recognition technology on both still and video imagery through a series of challenge problems and evaluation. The MBGC seeks to reach this goal through several technology development areas:

Face recognition on still frontal, real-world-like high and low resolution imagery Iris recognition from video sequences and off-angle images Fusion of face and iris (at score and image levels) Unconstrained face recognition from still and video Recognition from near infrared (NIR) and high definition (HD) video streams taken through portals The MBGC will consist of a set of challenge problems designed to advance the current state of technology and conclude with a planned independent evaluation. Challenge problems will focus on three major areas:

Iris and Face Recognition from Portal Video: the goal is to develop algorithms that recognize people from near infrared image sequences and high definition video sequences. The sequences will be acquired as people walk through a portal. Iris and Face Recognition from Controlled Images: the goal is to improve performance on iris and face imagery. Face data will be real-world-like high and low resolution images of frontal faces. Iris images will consist of still and video iris sequences. Still and Video Face: the goal is to advance recognition from unconstrained outdoor video sequences and still images. These challenge problems will allow for fusion of face and iris at both the score level and the image level.

Challenge Problem structure overview The Multiple Biometric Grand Challenge is based on previous challenges directed by Dr. P. Jonathon Phillips. Specifically the Facial Recognition Grand Challenge (FRGC) and the Iris Challenge Evaluation (ICE 2005). The programmatic process of a Challenge Problem is as follows. The Challenge Team designs the protocols, challenge problems, prepares challenge infrastructure, and composes the necessary data sets. Organizations then sign licenses to receive the data and begin to develop technology (mostly computer algorithms) in an attempt to solve the various challenges laid out by the Challenge Team. To advance and inform the various participants and interested parties the Team hosts workshops. The first workshop gives an overview of the challenge and introduces the first set of challenge problems (typically referred to as Version 1). The data sets are then released to participating organizations who develop their algorithms and submit self reported results back to the Challenge Team in the form of similarity matrices. The Team analyzes these results and then hosts another workshop. At the 2nd Workshop the Challenge Team reports the results from Challenge Version 1 and releases the Challenge Version 2. The cycle is repeated, finishing with a final workshop. At this stage the Participants are requested to submit not their self reported results, but the actual executables (or SDKs) to their algorithms. The Challenge Team then runs these algorithms through a battery of tests on large sequestered datasets. This phase ultimately determines the performance levels of the participant's algorithms. A final report is issued by the Team which is used by Industries and Governments to determine the actual state of the art in a given field and to provide participating organizations a basis for showing their performance within that field.

MBGC Challenge Version 1 The Multiple Biometric Challenge Version 1 was released in April 2008. This initial set of challenge problems had the following goals.

Familiarize community with problem and data. Introduce participants to challenge protocol and experiment environment. Grow the research community that works on these problems. 1st Characterization of the state of the art. The Version 1 series was separated into three distinct areas with various experiments under those areas.

Portal Challenge Still Iris versus Near Infrared (NIR) Video Iris versus Near Infrared (NIR) Still Face versus High Definition (HD) Video Multiple Biometrics (Fusion) Still Face / Still Iris versus Near Infrared (NIR) / High Definition (HD) Video Still Face / Video Iris versus Near Infrared (NIR) / High Definition (HD) Video Version 1 results were submitted in November 2008, and reported at the MBGC 2nd Workshop in December 2008.

MBGC Challenge Version 2 The MBGC Challenge Version 2 was released in January 2009. Results were reported at a workshop in December 2009.

Multiple Biometric Evaluation (MBE) The Multiple Biometric Evaluation (MBE) began in Summer 2009. The purpose of the MBE is to conduct an independent evaluation of the MBGC submissions on large sequestered data sets.

Sponsors Intelligence Advanced Research Projects Agency (IARPA) DOD Biometrics Task Force (BTF) Department of Homeland Security (DHS) FBI Criminal Justice Information Services Division Technical Support Working Group (TSWG)

… excerpt ends here. Continue reading the full article.

Illustrations

Worked examples

Example 1 — a first encounter with Multiple Biometric Grand Challenge

Start with the simplest possible case. Write down what Multiple Biometric Grand Challenge claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Multiple Biometric Grand Challenge before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Multiple Biometric Grand Challenge ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Multiple Biometric Grand Challenge

In research: Multiple Biometric Grand Challenge appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Multiple Biometric Grand Challenge in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Multiple Biometric Grand Challenge is common in secondary-school and first-year university syllabi. It links to neighbouring topics Biometrics, Facial recognition, so understanding it makes those chapters shorter.
In everyday life: Look for Multiple Biometric Grand Challenge outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Multiple Biometric Grand Challenge in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Multiple Biometric Grand Challenge means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Multiple Biometric Grand Challenge out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Multiple Biometric Grand Challenge in simple terms?

Why does Multiple Biometric Grand Challenge matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Multiple Biometric Grand Challenge?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Multiple Biometric Grand Challenge.