The document discusses the application of Hadoop in managing large-scale, low-latency distributed fuzzy matching databases for biometric data, highlighting its role in solving challenges associated with big data and unordered fuzzy matching. It covers various aspects of biometrics, including data operations for enrollment, verification, and identification, while emphasizing the importance of fuzzy matching due to the inherent noise in biometric data. The presentation also highlights the growth of biometric databases and proposes 'fuzzy table' as a solution for efficient and scalable low-latency searches.

1. Hadoop for Large-scale Biometric DatabasesJason TrostCloud Computing TeamBooz | Allen | Hamilton

2. This session shows the application of Hadoop and a large-scale, low-latency distributed fuzzy matching database to BiometricsBackground - what you need to know about BiometricsThe Problem – Big Data and unordered fuzzy matchingA Solution - Hadoop Applications for BiometricsSession Agenda

3. Key Takeaways from this SessionSearching large-scale Biometric Databases is a hard problemHadoop is a potential solution to this problemHadoop is a great platform for solving all sorts of Big Data and distributed computing problems, even low latency searching3

4. 4Introduction to BiometricsIrisFaceFingerprintBiometrics: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 matchingPalm PrintGaitHand GeometrySignatureEarVoiceKeystroke PatternFacial ThermogramVein Pattern* Handbook of Biometrics. A. Jain, P. Flynn A. Ross.

5. Assist with criminal investigations (e.g. crime scene fingerprints)Identify individuals entering and leaving the countrySurveillance5Why are Biometrics Important?Enables identifying/authenticating individuals based on “credentials” that are hard to forge

6. It has many useful applications where establishing identity is important

7. Banks and Financial Services companies are using biometrics to prevent banking and identity fraud

8. 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 existVerification – 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)6Biometric Database Operations

9. Enrollment: Adding New Identities and Biometrics Data to the DatabaseCollect biographic information from an individual such as name, address, SSN, etcCapture 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 database7

10. Verification: One-to-one MatchingLookup the biometric template for a particular individualVerify that the stored template and the recently captured template matchFuzzy matching is used for matching the biometric templates8

11. Identification: One-to-Many SearchingCapture some number of raw Biometric features, convert them into Biometric templatesPerform fuzzy matching against large number of stored biometric templates to determine the identityIf latency is not an issue, this is relatively straightforward, especially in MapReduceThis is a hard problem for low latency applications and increasing in complexity as the size of these databases growThere is a speed/accuracy tradeoffThe search space can be reduced using clustering techniques, but this only goes so far9

12. What is Fuzzy matching?Fuzzy matching is an operation performed on two objects that determines how similar the objects are to each otherTypically this operation produces a numeric similarity scoreNecessary when data collected from sensor is noisy, and matching needs to be very accurateAlmost all biometric matching algorithms perform some sort of fuzzy matching:Elastic Bunch Graph Matching – face recognition algorithmBOZORTH3 - minutiae based fingerprint matching algorithmIrisCode - iris matching algorithmOther Examples:Image comparisonAudio comparisonVideo comparison10

13. Why Fuzzy Matching?Biometric data is inherently noisy and dirtyConditions 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 companiesPartial or smudged fingerprints (e.g. crime scene)Changes in skin tone, facial hair, makeupDifferent lighting conditionsAging and skin damageWeight gain, Weight lossInjuryDerived from http://www.flickr.com/photos/glennji/3558118429/. Licensed under Creative Commons11

14. Existing Large-scale Biometric DatabasesUS Visitor & Immigrant Status Indicator Technology (US-VISIT)*International travelers’ biometrics (fingerprint and face)Collected at US ports of entry, Immigration Services, and State DepartmentUsed to support the Department of Homeland Security's missionFBI Integrated Automated Fingerprint Identification System, (IAFIS)**Used to solve and prevent crime and catch criminals and terroristsIncludes fingerprints, criminal histories, mug shots, scars and tattoo photos, physical characteristics like height, weight, and hair and eye color, and aliasesAllTrust Networks Paycheck Secure SystemUses fingerprints to support secure check cashingDesigned to stop fraud and speed check cashingPlus many more12* 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

15. This session shows the application of Hadoop and a large-scale, low-latency distributed fuzzy matching database to BiometricsBackground - what you need to know about BiometricsThe Problem – Big Data and unordered fuzzy matchingA Solution - Hadoop Applications for BiometricsSession Agenda

16. Combined U.S. government biometric databases are expected to grow to hold billions of identitiesThe 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*****13Growth 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

17. Biometric Databases are a Big Data ProblemLarge scale operationsSearching and storing 100 Million to 1 Billion IdentitiesMultiple 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 timeRaw Images:(500M Identities x 16KB-300KB* x 10-20) = 1-2 PBBiometric Templates:(500M Identities x 256b-3KB** x 10-20) = 2-27 TB15

18. Biometric Databases Must Perform Fuzzy MatchingFuzzy matching techniques must be used because the data is noisy and “dirty”

19. Most applications require low latency fuzzy match searches in order to be useful

20. The objects being searched for cannot be ordered effectively to speed up searches

21. Clustering techniques can be used to reduce the search space, but this only goes so far

22. Fuzzy match searches are expensive and typically a large number of objects need to be searched to find a match16

23. This session shows the application of Hadoop and a large-scale, low-latency distributed fuzzy matching database to BiometricsBackground - what you need to know about BiometricsThe Problem – Big Data and unordered fuzzy matchingA Solution - Hadoop Applications for BiometricsSession Agenda

24. Hadoop and Biometric DatabasesHDFS as file storage for petabytes worth of imagesRedundancyDistributionOpens the doors to storing more and more raw images and at higher resolutions18Mahout/MapReduce can be used for indexing and clustering biometric templates to improve overall search speeds

25. MapReduce can be used for improving feature selection by analyzing the entire database to select features that are most effective in distinguishing identities

26. Easy to test and deploy new algorithms against all data at scale

27. N-to-N matching search (special type of Identification search) to cleanse database, find people trying to circumvent the system (Identity Fraud, etc)

28. Map Reduce can be used for batched searching where latency doesn’t matter

29. What about low latency searching…?Fuzzy Table: A Solution to Large-scale, Low Latency, Fuzzy MatchingFuzzy Table is a large scale, low latency, distributed fuzzy matching databaseIt 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, etcIt provides the benefits of Hadoop against problems that require large scale low latency fuzzy matchingHorizontal scalability over commodity hardwareDistributed and parallel computationHigh reliability and redundancyEnabled by Mahout and MapReduce for binning/clustering, re-encoding, and other bulk data operationsWe have found no other solution with these characteristics19

30. Fuzzy Table Architecture20

31. Fuzzy Table: Bulk Data Processing ComponentThe 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 searchingMapReduce is used for all other bulk or batch data processing including:Re-encoding the raw files into Feature vectorsPerforming large-scale feature evaluation to improve clusteringBatch fuzzy match searching21Mahout’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

32. This makes searching faster because a only small subset of the data must be processed

33. 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

34. Bulk Clustering and Real-time Classification22This makes searching for keys faster because only a small subset of the entire dataset needs to be processed using fuzzy matchingThe classifier determines which Bins need to be searched in order to find the most likely matching keys

35. Fuzzy Table: Data Storage and BinsBins are represented as directories in HDFS containing one or more chunk files (stored as SequenceFiles): /fuzzytable/_table_fingerprints/_bin_000001/_chunk_000001Chunk 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 clusterThis ensures that the bins are striped across the cluster for optimal parallel searchingAlso, chunk files are replicated across the Data Servers using the replication mechanism in HDFSData Servers only search through chunk files that reside locally and results are returned in real-time as soon as a match is found23

36. Fuzzy Table: Low Latency Fuzzy Matching ComponentThe low latency component consists of three main partsClient – submit queries for Keys and get back {Key, Value} pairsMaster Server – serve metadata about which Data Servers host which binsData Servers – Actually perform fuzzy matching searchesData Servers perform fuzzy matching against Keys in order to find {Key, Value} recordsdouble score = fuzzyMatcher.match(key, storedRec.getKey());if(score >= threshold) return storedRec;Fuzzy matching searches are performed in parallel across many Data Servers24

37. Fuzzy Table Query25

38. Fuzzy Table Query26