patent analysis of biometrics technology - department … · patent analysis of biometrics...

Patent Analysis of Biometrics

Technology MIS 480/580 - Project Report

Shaokun Fan | Noyan Ilk | Pooja Bhandari

With the guidance of:

Dr. Hsinchun Chen

Professor, Management Information Systems

Eller College of Management

University of Arizona

Patent Analysis of Biometrics Technology

Team 2

Table of Contents 1. Introduction .......................................................................................................................................... 3

2. Literature Review .................................................................................................................................. 3

3. Research Objectives .............................................................................................................................. 4

4. Data Source ........................................................................................................................................... 5

5. Data Analysis ......................................................................................................................................... 6

5.1. Patent Publication Analysis ........................................................................................................... 6

5.2. Topic Coverage Analysis ................................................................................................................ 9

5.3. Patent Citation Analysis .............................................................................................................. 10

6. Conclusions ......................................................................................................................................... 15

7. References .......................................................................................................................................... 15


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1. Introduction

Biometrics refers to automatic recognition of an individual based on her behavioral and/or

physiological characteristics. A biometric system is essentially a pattern recognition system that

operates by acquiring biometric data from an individual, extracting a feature set from the acquired

data, and comparing this feature set against the template set in the database [1].

The applications of biometrics can be divided into the following two main groups:

Commercial applications such as computer network login, electronic data security, ecommerce,

Internet access, ATM, credit card, physical access control, cellular phone, PDA, medical records

management, distance learning, etc.

Government applications such as national ID card, correctional facility, driver’s license, social

security, welfare-disbursement, border control, passport control, criminal investigation, etc.

Biometric traits can mainly be classified into two groups: Physiological and Behavioral. Physiological

biometrics includes face, fingerprint, hand, iris and DNA recognition. Behavioral biometric traits involve

keystroke patterns, signature analysis and voice recognition [2]. In this context, we propose to conduct

a knowledge mapping research on patents involving biometrics technologies and applications.

Specifically, we plan to analyze and compare patents based on classification schemes, systems and

developers of biometrics.

2. Literature Review

A number of biometric characteristics exist and are in use in various applications [2]. Each biometric has

its strengths and weaknesses, and the choice depends on the application. No single biometric is

expected to effectively meet the requirements of all the applications. In other words, no biometric is

“optimal”. The match between a specific biometric and an application is determined depending upon

the operational mode of the application and the properties of the biometric characteristic. A brief

introduction of the commonly used biometrics is given below:

Fingerprint [3]: Humans have used fingerprints for personal identification for many centuries

and the matching accuracy using fingerprints has been shown to be very high. A fingerprint is


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the pattern of ridges and valleys on the surface of a fingertip, the formation of which is

determined during the first seven months of fetal development. Fingerprints of identical twins

are different and so are the prints on each finger of the same person.

Face [4]: Face recognition is a non-intrusive method, and facial images are probably the most

common biometric characteristic used by humans to make a personal recognition.

DNA [5]: Deoxyribo Nucleic Acid (DNA) is the one-dimensional ultimate unique code for one’s

individuality - except for the fact that identical twins have identical DNA patterns.

Signature: The way a person signs her name is known to be a characteristic of that individual.

Although signatures require contact with the writing instrument and an effort on the part of

the user, they have been accepted in government, legal, and commercial transactions as a

method of verification. Signatures are a behavioral biometric that change over a period of time

and are influenced by physical and emotional conditions of the signatories.

Voice: Voice is a combination of physiological and behavioral biometrics. The features of an

individual’s voice are based on the shape and size of the appendages (e.g., vocal tracts, mouth,

nasal cavities, and lips) that are used in the synthesis of the sound. These physiological

characteristics of human speech are invariant for an individual, but the behavioral part of the

speech of a person changes over time due to age, medical conditions (such as common cold),

emotional state, etc.

3. Research Objectives

The main objective of this research project is to extract information from the US Patent Office and

assess the research and development status of biometrics. We also want to analyze the growth of

biometrics sub-fields and identify interesting historical trends. More specifically, we are looking to

figure out:

Number of patents by each country each year

Citation clustering

Number of patents each year by category, such as fingerprint, facial, etc.

Number of patents each institution


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4. Data Source

During the data collection phase of the project, the team first did a domain analysis based on several

surveys and books in the field of biometrics [1], [6]. Then we defined a set of keywords and did our test

search in USPTO. After we manually retrieved some patents from USPTO, we went through those

patents one by one and identify the keywords that are common in those patents. In order to make sure

that our keyword set is a relatively complete, we also compared our keywords with those in wikipedia

[2] and google keywords (https://adwords.google.com/select/KeywordToolExternal). In the end, our

keyword set includes 75 keywords which are shown in our presentation slides.

The keyword list was then used to search the USPTO database website. The programming source code

used to search the database was provided by Teaching Assistant Mandy Dang. The following steps were

used to retrieve and format the patent data:

• Patent ID retrieval – A Perl script was used to search the USPTO website and retrieve all patent

numbers related to each keyword. This program returned a list of keywords and the related

patent numbers.

• Patent fulltext retrieval – A Perl script was used to search the USPTO website and retrieve the

patent source content for each patent number. The program returned the patent content in

HTML format (one HTML file per patent).

• Data Parsing – A Java program was used to parse the data in the HTML files into text files. The

program returned eight text files which are later imported into database.

• Data Cleaning – Duplicated patents are removed.

https://adwords.google.com/select/KeywordToolExternal


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5. Data Analysis

5.1. Patent Publication Analysis

5.1.1. Number of patents by Country

Figure 1: Number of patents by country

Figure 1 illustrates the total number of Biometric patents issued by country from year 1976-2008. It is

apparent that the US is the world leader followed by Japan. The United States has produced 3,079

patents, followed by Japan with 857, making up 65.8% of the total patents issued respectively. The

figure 2 gives the year wise distribution of number of patents issued by the Top 5 countries from year

2000-2008. Considering the US and Japan historical trends there was large growth in this area during

year 2005-2006 since highest number of patents were filed during this period.

Figure 2: Number of patents by country and year


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5.1.2. Number of patents by Country group

Figure 3: Number of patents by country group

The figure 3 shows the number of patents by the country group. Here North America including

United States and Canada takes the lead again, followed by Japan. Europe is number three and

includes the countries Germany, Great Britain, France, Netherlands, Finland, Sweden and Italy. Asia-

Pacific includes Korea, Singapore, China, Taiwan and Hong Kong.

5.1.3. Number of patents by Institution

Figure 4: Number of patents by Institution / Institution and year

Figure 4 provides information about the number of patents by institution and these institutions’ yearly

trends. Here, we see that IBM has been the leader, even if its patent numbers approved have steadily

fallen on years 2004, 2005 and 2007. Microsoft partially follows a similar trend as IBM’s, where the

drop period is between years 2006 and 2007.


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5.1.4. Number of patents by Technology fields

Figure 5: Number of patents by Technology fields

Figure 5 provides number of patents by technology fields. These fields are obtained from US class

definitions formally made by USPTO.

5.1.5. Patent pending period statistics

Figure 6: Patent Period Statistics


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On the left side of figure 6, we can see the distribution of number of patents approved with respect to pending

periods. We see that it is very rare for a patent to get approved in one or two years, or be pended 8 years and

more. The data tells us that, on average, it would take 4.3 years for a patent to get approved. On the right side of

the figure we see the average patent approval period with respect to particular years. In 2000, the average time

for a patent to get approved was 2.5 years, whereas this number went up to more than 4 years for 2008. This

information indicates that while more patents are being filed every year, the US Patent Office has not been able to

improve itself and keep its patent analysis rate steady. These data have been obtained by looking at the difference

between patent approval and patent filing times.

5.2. Topic Coverage Analysis

Figure 7: Biometrics Key Domains

On figure 7, we see that the key areas in biometrics patents have been speech and finger print research so far. In

depth analysis of fingerprint patents define the following key sub areas shown in figure 8 below.

Figure 8: Fingerprint Key Sub-areas


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5.3. Patent Citation Analysis

5.3.1. Top 10 Cited Patents

Name # cited App. Date Institution

Unforgeable personal identification system 210 12-Feb-91 Motorola, Inc.

Fingerprint sensing device and recognition system having

predetermined electrode activation 123 28-Jun-94

U.S. Philips

Corporation

Method for interactive speech recognition and training 105 25-Jun-91 Dragon Systems, Inc.

Method and apparatus for verifying identity using image correlation 103 19-Nov-91 Identix Incorporated

Fingerprint-based access control and identification apparatus 102 1-Jul-80 Fingermatrix, Inc.

System for verifying use of a credit/identification card including

recording of physical attributes of unauthorized users 99 30-Apr-96 Wizards, LLC

Continuous biometric authentication matrix 97 20-Jul-93

Data carrier 96 15-Apr-86

Capacitive fingerprint sensor 96 5-Oct-82 Siemens Corporation

Arrangement and procedure for determining the authorization of

individuals by verifying their fingerprints 95 19-Feb-91

Siemens

Aktiengesellschaft

Table 1: Top 10 Cited Patents

Table 1 above shows the top 10 cited patents in biometrics area including their names, how many

times they are cites, their approval dates and institutions. Here, we see several fingerprint patents as

highly cited. This is expected since about 30% of the patents in the biometrics area are fingerprint

related. We see that the highest number of citation is 210, a patent from Motorola, Inc. organization.

5.3.2. Top 5 Citing Patents

Name # cited Institution

Authenticating fingerprints for radio frequency payment transactions 999 Xatra Fund MX, LLC

Biometrics for radio frequency payment transactions 999 Xatra Fund MX, LLC

Smartcard transaction method and system using voiceprint recognition 999

American Express

Services Company

Biometric security using a fob 999 Xatra Fund MX, LLC

Method and system for proffering multiple biometrics for use with a FOB 971

American Express

Services Company

Table 2: Top 5 Citing Patents


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In table 2, we can see the top 5 citing patents. This information is not very interesting by itself, since a

patent can have any number of citings in it. However, we see a pattern in institutions that create these

highly cited patents, which may be an information of value. Another thing we realize is the limit of

citings in USPTO database. It is very likely that the citing numbers 999 are actually higher, but USPTO

only stores to this amount.

5.3.3. Average number of cites by Country

Country Total no. of patents Total no. of cites Average number of cites

Canada 115 1143 9.94

United States 3079 16062 5.22

Japan 857 3420 3.99

Germany 136 345 2.54

South Korea 73 173 2.37

Table 3: Average number of cites by Country

Table 3 provides information on the average number of cites by country. Average numbers are

calculated by dividing the total number of cites to a particular country to total number of patents the

country has. The results are surprising here; Canada, which was number four in the total number of

patents rankings in the Biometrics area, turns out to be the mostly cited country on average, almost

doubling the nearest competitor – United States.

5.3.4. Average number of cites by Institution

Institution Total no. of patents Total no. of cites Average no. of cites

Lucent Technologies Inc. 70 377 5.39

NEC Corporation 127 674 5.31

Motorola, Inc. 75 372 4.96

International Business

Machines Corporation 380 1493 3.93

Canon Kabushiki Kaisha 59 231 3.92


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Matsushita Electric

Industrial Co., Ltd. 71 188 2.65

AT&T Corp. 58 144 2.48

Microsoft Corporation 137 240 1.75

Sony Corporation 114 145 1.27

Intel Corporation 63 72 1.14

Table 4: Average number of cites by Institution

Table 4 provides information on the average number of cites by Institution. Here, we see that Lucent

Technologies, who was ranked seven in total number of patent ranking, is the mostly cited institution

on average. Average cites rankings are thought to be an indication of the quality/importance of patents

these institutions generate.

5.3.5. Country citation network analysis

Below is Figure 9, which is a graphical representation of citation network between the top five patent

produced countries in the Biometrics area. The number near to the country name on the line

represents the number of citations made from that country to the country at the other end of the line.

The thickness of the lines and arrow heads are proportional to the number of citations.

Figure 9: Country Citation Network


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5.3.6. Institution citation network analysis

Similar to Figure 9, Figure 10 shows the citation network between the top 10 patent produced

institutions.

Figure 10: Institution Citation Network

5.3.7. Institution Clustering based on patent Citations

Based on citation references between institutions, we have applied a simple clustering implementation

to reveal any possible inter-relationships between organizations. First of all, we have converted the

directed links to undirected by merging incoming and outgoing links between any given set of

institutions. Then, we have applied Jaccard measure to calculate similarity ratios between all

institutions. Figure 11 provides information on the outgoing links, similar references between and

finally Jaccard similarity ratios between organizations.


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Figure 11: Similarity Calculations between Institutions

Figure 12: Clustering Result of Institutions as Dendogram and Sets

Figure 12 above provides the classification information of institutions based on citations after the single

linkage clustering based on Jaccard similarities. The left side of the figure shows the dendogram tree,

while the right side is a representation of institutions as sets where dotted lines represent identified

clusters.


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6. Conclusions

To summarize, we have identified some potentially significant information from our analysis of the

Biometrics patents data. We found out that the US is the leading patent holder in Biometrics area, with

more than 2/3 of all patents approved. However, based on citation averages, Canada takes the lead in

terms of producing the important patents. In terms of patent pending period statistics, we see an

increase in the average patent approval period through the years. This information is consistent with

the increasing number of patents being filed every year. Overall, we couldn’t see that the US Patent

Office would be able to match the speed of patent filing rate in terms of analyzing the patents and

making the approval decision. We also found out that the key areas in biometrics patents have been

speech and finger print research so far. Finally, another interesting result with this study was that we

managed to cluster institutions based on patent citations. We have seen that there are indeed small

clusters of institutions that cite each other more often than citing any other institutions outside the

cluster. Overall, this was a very valuable study that made us learn about the automation of vast

amount of data collection from external resources, filtering and cleaning collected data, and analyzing

the data to test assumptions and reveal any underlying patterns such as trends and clusters.

7. References

[1] Jain, A. K.; Ross, Arun; Prabhakar, Salil, "An introduction to biometric recognition", IEEE

Transactions on Circuits and Systems for Video Technology Vol. 14, No.1, pp. 4 – 20, 2004.

[2] http://en.wikipedia.org/wiki/Biometrics

[3] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer,

NY, 2003.

[4] L. Hong and A. K. Jain, “Integrating Faces and Fingerprints for Personal Identification”, IEEE Trans.

on Pattern Analysis and Machine Intelligence, Vol. 20, No. 12, pp. 1295-1307, Dec 1998.

[5] R. Brunelli and D. Falavigna, “Person Identification Using Multiple Cues”, IEEE Trans. On Pattern

Analysis and Machine Intelligence, Vol. 12, No. 10, pp. 955-966, Oct 1995.

[6] David D. Zhang, “Biometric solutions for authentication in an E-world,” Kluwer academic

publishers, 2002, ISBN 1402071426.

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