Biometric Databases and Hadoop__HadoopSummit2010

Hadoop for Large-scale Biometric Databases
Jason Trost
Cloud Computing Team
Booz | Allen | Hamilton

This session shows the application of Hadoop and a large-scale, low-latency distributed fuzzy matching database to Biometrics
Background - what you need to know about Biometrics
The Problem – Big Data and unordered fuzzy matching
A Solution - Hadoop Applications for Biometrics
Session Agenda

Key Takeaways from this Session
Searching large-scale Biometric Databases is a hard problem
Hadoop is a potential solution to this problem
Hadoop is a great platform for solving all sorts of Big Data and distributed computing problems, even low latency searching
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Introduction to Biometrics
Iris
Face
Fingerprint
Biometrics:
The science of establishing the identity of an individual based on the physical, chemical, or behavioral attributes of the person *
Modality:
Physical or behavioral characteristics of an individual used to establish identity*
Template:
A symbolic or numeric representation of a modality optimized for storage and/or matching
Palm Print
Gait
Hand Geometry
Signature
Ear
Voice
Keystroke Pattern
Facial Thermogram
Vein Pattern
* Handbook of Biometrics. A. Jain, P. Flynn A. Ross.

Assist with criminal investigations (e.g. crime scene fingerprints)
Identify individuals entering and leaving the country
Surveillance
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Why are Biometrics Important?
Enables identifying/authenticating individuals based on “credentials” that are hard to forge
It has many useful applications where establishing identity is important
Banks and Financial Services companies are using biometrics to prevent banking and identity fraud
National governments are creating biometric databases for law enforcement & security reasons:

Enrollment – Add an identity and associated biometric data to the database if they do not already exist
Verification – Lookup the biometric template for a single individual and determine whether it matches a captured biometric measurement (1-to-1 match)
Identification – Determine the identity of an individual given some biometric measurements (1-to-N match)
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Biometric Database Operations

Enrollment: Adding New Identities and Biometrics Data to the Database
Collect biographic information from an individual such as name, address, SSN, etc
Capture biometric data in raw form (e.g. high resolution images)
Transform raw biometric data into encoded biometric template (feature vector)
Store all this information in the biometrics database
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Verification: One-to-one Matching
Lookup the biometric template for a particular individual
Verify that the stored template and the recently captured template match
Fuzzy matching is used for matching the biometric templates
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Identification: One-to-Many Searching
Capture some number of raw Biometric features, convert them into Biometric templates
Perform fuzzy matching against large number of stored biometric templates to determine the identity
If latency is not an issue, this is relatively straightforward, especially in MapReduce
This is a hard problem for low latency applications and increasing in complexity as the size of these databases grow
There is a speed/accuracy tradeoff
The search space can be reduced using clustering techniques, but this only goes so far
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What is Fuzzy matching?
Fuzzy matching is an operation performed on two objects that determines how similar the objects are to each other
Typically this operation produces a numeric similarity score
Necessary when data collected from sensor is noisy, and matching needs to be very accurate
Almost all biometric matching algorithms perform some sort of fuzzy matching:
Elastic Bunch Graph Matching – face recognition algorithm
BOZORTH3 - minutiae based fingerprint matching algorithm
IrisCode - iris matching algorithm
Other Examples:
Image comparison
Audio comparison
Video comparison
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Why Fuzzy Matching?
Biometric data is inherently noisy and dirty
Conditions are not exactly the same when the original biometric data was captured (Enrollment) and when a new reading occurs (Identification)
Different types of cameras and sensors made by different companies
Partial or smudged fingerprints (e.g. crime scene)
Changes in skin tone, facial hair, makeup
Different lighting conditions
Aging and skin damage
Weight gain, Weight loss
Injury
Derived from http://www.flickr.com/photos/glennji/3558118429/. Licensed under Creative Commons
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Existing Large-scale Biometric Databases
US Visitor & Immigrant Status Indicator Technology (US-VISIT)*
International travelers’ biometrics (fingerprint and face)
Collected at US ports of entry, Immigration Services, and State Department
Used to support the Department of Homeland Security's mission
FBI Integrated Automated Fingerprint Identification System, (IAFIS)**
Used to solve and prevent crime and catch criminals and terrorists
Includes fingerprints, criminal histories, mug shots, scars and tattoo photos, physical characteristics like height, weight, and hair and eye color, and aliases
AllTrust Networks Paycheck Secure System
Uses fingerprints to support secure check cashing
Designed to stop fraud and speed check cashing
Plus many more
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* One Team, One Mission, Securing our Homeland. US DHS.
** http://www.fbi.gov/hq/cjisd/iafis/iafis_facts.htm
*** http://www.alltrustnetworks.com/News/6Million/tabid/378/Default.aspx

Session Agenda

Combined U.S. government biometric databases are expected to grow to hold billions of identities
The DHS’s US-VISIT program has the world’s largest and fastest biometric database (called IDENT) with over 110 million identities and roughly 145,000 identities enrolled or verified daily*
From the FBI’s Integrated Automated Fingerprint Identification System (IAFIS) alone, there are 66.5 million identities with 8,000-10,000 more subjects added each day **
India is reportedly creating a biometric database to hold the fingerprints and face images for each of its 1.2 billion citizens as part of its Unique Identification Project ***
European Union’s Biometric Matching System (EU-BMS) is expected to hold biometric information of 70 Million people to support visa applications, border control, and immigration ****
AllTrust Networks Paycheck Secure system has enrolled over 6 Million users and has performed over 70 Million transactions*****
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Growth of Biometric Databases
* US-VISIT: The world’s largest biometric application. William Graves.
** http://www.fbi.gov/hq/cjisd/iafis/iafis_facts.htm
*** http://www.business-standard.com/india/news/national-population-register-to-start-biometrics-data-collectiondec/399135/
**** http://www.findbiometrics.com/articles/i/5220/
***** http://www.alltrustnetworks.com/News/6Million/tabid/378/Default.aspx

Biometric Databases are a Big Data Problem
Large scale operations
Searching and storing 100 Million to 1 Billion Identities
Multiple biometric templates and raw files per identity for multimodal matching (Fingerprints, Faces, and Iris)
Typically, new raw files and templates are stored after each Verification and Identification operation because the biometrics readings change over time
Raw Images:
(500M Identities x 16KB-300KB* x 10-20) = 1-2 PB
Biometric Templates:
(500M Identities x 256b-3KB** x 10-20) = 2-27 TB
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Biometric Databases Must Perform Fuzzy Matching
Fuzzy matching techniques must be used because the data is noisy and “dirty”
Most applications require low latency fuzzy match searches in order to be useful
The objects being searched for cannot be ordered effectively to speed up searches
Clustering techniques can be used to reduce the search space, but this only goes so far
Fuzzy match searches are expensive and typically a large number of objects need to be searched to find a match
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Session Agenda

Hadoop and Biometric Databases
HDFS as file storage for petabytes worth of images
Redundancy
Distribution
Opens the doors to storing more and more raw images and at higher resolutions
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Mahout/MapReduce can be used for indexing and clustering biometric templates to improve overall search speeds
MapReduce can be used for improving feature selection by analyzing the entire database to select features that are most effective in distinguishing identities
Easy to test and deploy new algorithms against all data at scale
N-to-N matching search (special type of Identification search) to cleanse database, find people trying to circumvent the system (Identity Fraud, etc)
Map Reduce can be used for batched searching where latency doesn’t matter
What about low latency searching…?

Fuzzy Table: A Solution to Large-scale, Low Latency, Fuzzy Matching
Fuzzy Table is a large scale, low latency, distributed fuzzy matching database
It enables fast parallel searches against keys that cannot be effectively ordered and that require fuzzy matching such as biometrics identification, large scale image search, large scale audio search, etc
It provides the benefits of Hadoop against problems that require large scale low latency fuzzy matching
Horizontal scalability over commodity hardware
Distributed and parallel computation
High reliability and redundancy
Enabled by Mahout and MapReduce for binning/clustering, re-encoding, and other bulk data operations
We have found no other solution with these characteristics
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Fuzzy Table Architecture
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Fuzzy Table: Bulk Data Processing Component
The centroids from K-means clustering are used to create a “Bin classifier” that is used determine the best bins to search for a given key
{Key, Value} records are stored as SequenceFiles in HDFS and the files are stored in such a way to spread these records across the cluster for optimal parallel searching
MapReduce is used for all other bulk or batch data processing including:
Re-encoding the raw files into Feature vectors
Performing large-scale feature evaluation to improve clustering
Batch fuzzy match searching
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Mahout’s Canopy Clustering and K-means Clustering are used to partition the data into clusters (referred to as bins) in order to reduce the search space
This makes searching faster because a only small subset of the data must be processed
This concept is based on work done in academia*
*Efficient Search and Retrieval in Biometric Databases by Amit Mhatre, Srinivas Palla, Sharat Chikkerur and Venu Govindaraju
* Efficient fingerprint search based on database clustering. Manhua Liu, Xudong Jiang, Alex Chichung Kot

Bulk Clustering and Real-time Classification
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This makes searching for keys faster because only a small subset of the entire dataset needs to be processed using fuzzy matching
The classifier determines which Bins need to be searched in order to find the most likely matching keys

Fuzzy Table: Data Storage and Bins
Bins are represented as directories in HDFS containing one or more chunk files (stored as SequenceFiles): /fuzzytable/_table_fingerprints/_bin_000001/_chunk_000001
Chunk files contain many {Key, Value} pairs and are a small multiple of the HDFS block size
Chunk files are distributed uniformly and randomly across the Data Servers in the cluster
This ensures that the bins are striped across the cluster for optimal parallel searching
Also, chunk files are replicated across the Data Servers using the replication mechanism in HDFS
Data Servers only search through chunk files that reside locally and results are returned in real-time as soon as a match is found
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Fuzzy Table: Low Latency Fuzzy Matching Component
The low latency component consists of three main parts
Client – submit queries for Keys and get back {Key, Value} pairs
Master Server – serve metadata about which Data Servers host which bins
Data Servers – Actually perform fuzzy matching searches
Data Servers perform fuzzy matching against Keys in order to find {Key, Value} records
double score = fuzzyMatcher.match(key, storedRec.getKey());
if(score >= threshold)
return storedRec;
Fuzzy matching searches are performed in parallel across many Data Servers
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Fuzzy Table Query
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Fuzzy Table Query
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Fuzzy Table Query
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Fuzzy Table Query
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Fuzzy Table Query
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Fuzzy Table Query
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Fuzzy Table Query
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Fuzzy Table Query
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Future Work
Fuzzy Table is still a research prototype, but we plan to keep building it out to support this biometrics work
Locality Sensitive Hashing instead of K-means clustering for binning and search space reduction
Distributed/Replicated master servers (and Zookeeper integration)
Real-time ingest
Hopefully we will have performance/scalability metrics as well as more features and example applications to share within the next few months
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Conclusion
Searching large-scale Biometric Databases is a hard problem
Hadoop is a potential solution to this problem
We used MapReduce for bulk processing to enable distributed low latency fuzzy matching over HDFS
Hadoop is a great platform for solving all sorts of Big Data and distributed computing problems, even for low latency searching
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Contributors
Cloud Computing Team
Jason Trost
Lalit Kapoor
Daniel Neuberger
Michael Beck
Edmond Kohlwey
Josh Sullivan
Identity Management/Biometrics Team
Abel Sussman
Eric Karlinsky
Deanna Walters
Joel Rader
Allen Wight
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Questions?

Contact Information – Cloud Computing Team
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Joshua Sullivan
Senior Associate
Lalit Kapoor
Senior Consultant
Michael Beck
Senior Consultant
Daniel Neuberger
Senior Consultant
Jason Trost
Associate
Booz Allen Hamilton Inc.
134 National Business Parkway.
Annapolis Junction, Maryland 20701
(301)543-4611
sullivan_joshua@bah.com
(301)821-8000 kapoor_lalit@bah.com
(301)543-4400
trost_jason@bah.com
Edmund Kohlwey
Consultant
(301)617-3523 kohlwey_edmund@bah.com

Contact Information – Identity Management Team
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Joel Rader
Identity Analyst
Eric Karlinsky
Identity Analyst
Deanna Walters
Biometrics Analyst
Allen Wight
Biometrics Analyst
13200 Woodland Park Rd
Herndon, VA 20171
(703) 984-0312
rader_joel@bah.com
13200 Woodland Park Rd.
Herndon, VA 20171
(703) 984-3532 Karlinsky_eric@bah.com
Herndon, VA 20171
(703) 984-1982
walters_deanna@bah.com
Herndon, VA 20171
(703) 984-1978
wight_allen@bah.com
Abel Sussman
Biometrics Subject Matter Expert
13200 Woodland Park Rd.
Herndon, VA 20171
(703) 984-7663
sussman_abel@bah.com

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