THE FUTURE OF NFIQ – MARCH 1, 2010 

1. Background  

Quality measurement plays vital role in improving biometric system accuracy and efficiency during the capture process 

(as  a  control‐loop  variable  to  initiate  reacquisition),  in  database  maintenance  (sample  update),  in  enterprise‐wide 

quality  assurance surveying,  and  in  invocation  of  quality‐directed  processing  of samples.  If  quality  can  be  improved, 

either by sensor design, by user interface design, or by standards compliance, better performance can be realized. For 

those aspects of quality that cannot be designed‐in, an ability to analyze the quality of a live sample is needed.  This is 

useful primarily in initiating  the reacquisition  from a user, but also  for  the real‐time selection of  the best sample, and 

the selective invocation of different processing methods. 

Biometric  quality  analysis  is  a  technical  challenge  because  it  is  most  helpful  when  the  quality  measures reflect  the 

performance sensitivities of one or more target biometric matchers.  NIST addressed this problem in August 2004 when 

it  issued  NIST  Fingerprint  Image  Quality  (NFIQ)  algorithm.  NFIQ  is  a  fingerprint  quality  measurement  tool;  it  is 

implemented as open‐source software; and is used today in U.S. government and commercial deployments.  NFIQ is a 

machine‐learning algorithm and its key innovation is to produce a quality value from a fingerprint image that is directly 

predictive  of  expected  matching  performance.  NFIQ serves  as  a  publicly  available  reference  implementation.  With 

advances in fingerprint technology since 2004, an update to NFIQ is needed.  

2. Statement of Need 

NFIQ  has  been  designed  to  be  matcher  independent.  For  applications  where  matching  algorithm  is  not  known  or 

subject to change, a generalized (i.e. matcher independent) quality is needed.  However, when comparison algorithm is 

known  use  of  quality  assessment  algorithm tuned to  predict the  performance  of the  comparison  algorithm  is more 

suitable.  Therefore  the  next  generation  of  finger  image  quality, should  provide  both  options  of  “generalized”  (i.e. 

matcher‐independent) or “dedicated” (i.e. matcher dependent).  

Interoperability  of  quality scores  is  another  challenge  in  exchange  of  quality scores.  Part  1  of the multipart  ISO/IEC 

29794 Biometric sample quality standard defines a binary record structure for the storage of a sample’s quality data. It 

establishes requirements on the syntax and semantic content of the structure.  Specifically, it states that the purpose of 

assigning a quality score to a biometric sample shall be to indicate the expected utility of that sample in an automated 

comparison  environment.  That  is,  a  quality  algorithm should  produce  quality scores that target  application‐specific 

performance  variables.  For  verification, the metric  would  usually  be  false‐match  and  false‐non‐match rates that  are 

likely to be realized when the sample is matched.  This, by itself, is not sufficient for accurate interpretation of quality 

scores generated by different quality assessment algorithms and some normalization or calibration is needed. 

The goal of this workshop is to address the technical short‐falls in current quality assessment technology, and aims to 

engage  industry to  improve  core  finger  image  quality  assessment technology  based  on  lessons  learned  from recent 

deployments of quality assessment algorithms (including NFIQ) in large‐scale identity management applications. 

3. Finding a way forward 

We see three possible options for future of NFIQ. 

1. The first and probably least attractive option is to do nothing.   Stop the work on NFIQ. 

2. The second  and  more reasonable  option  is to  do  incremental  upgrade/revision to  NFIQ  1.0.  Examples  of such 

improvements  are:  inclusion of  core  in the feature  vector (to overcome the  issue with  impressions of tip only); 

more resolution (0‐100 instead of 1‐5 levels); re‐train with newer data and/or newer matcher; and train on rolled 

impression. 

3. The third, and our proposed option is, in consultation and collaboration with industry, implement various modular 

versions of NFIQ.  This option is explored in more details below. 

ELHAM.TABASSI[AT]NIST[DOT]GOV 
THE FUTURE OF NFIQ – MARCH 1, 2010 

3.1. Proposed scientific approach for modular NFIQ 

3.1.1. Feature Extraction 

We  propose  development  of  a  set  of  standardized  finger  image  quality  components  plus  multivariate  statistics 

techniques to relate biometric performance metrics such as false non‐match to the standardized or vendors’ generated 

quality  components  (i.e.  features).    The  outcome  will  be  modular  quality  algorithms  (NFIQ  2.0)  with  plug‐and‐play 

feature vector extraction algorithms that could be performed by a vendor, an open source algorithm, or a combination 

of the two.   

This task requires close collaboration with providers. 

Development of modular NFIQ 2.0, which by design, could be either generalized or specialized to a particular 

comparison algorithm.  As with NFIQ, the new NFIQ 2.0 will have two major computation steps: 

• Feature extraction, and 

• Training of a machine‐learning algorithm. 

Feature  extraction  consists  of  measuring  appropriate  image  characteristics  that  convey  information  for  comparison 

algorithms.  The feature set  is  comprised  of  elements such  as  local  noise,  continuity  of ridge flow,  area  of the finger 

image  impression,  and  number  of minutiae.  A feature  vector  is  computed from  each  image  and  its  component  are 

combined using a trained machine‐learning algorithm so that the image quality score is reflective of positive or negative 

contribution of the sample to the overall performance of the system.   

Modular NFIQ allows for dedicated quality assessment (feature extraction and training tuned to a particular algorithm) 

or generalized and matcher independent quality assessment to be used in any application without the knowledge of the 

deployed (template generation or matcher) deployed.  A brief description of our proposed NFIQ 2.0 straw‐man design 

follows. 

Modular NFIQ 2.0 supports plug‐and‐play feature vectors. Three separate options are considered here: 

1. Black Box Feature Vector 

Vendors can supply their proprietary (black box) feature extraction software as an .exe or a complied library.  This 

converts an image into a proprietary quality vector.  This option generates a “dedicated” (i.e. extraction algorithm 

dependent) quality assessment  tool.  Incorporating vendor proprietary  feature vectors, improves  the efficiency of 

quality computations in  the  field because  the  feature vector computation is  potentially a by‐product of  template 

generation.  Vendors’  intellectual  property  is  reserved.  The  success  of  black  box  plan  depends  on  vendor 

participation.  

2. Clear Box Feature Vector 

Optionally,  in collaboration  with  industry,  a  set  of  quality  components  will  be  defined  and  perhaps  formally 

standardized.  These quality components shall model failure modes and sensitivities of current fingerprint matching 

algorithms.  Examples include: zonal quality, clarity of ridges, size of fingerprint, or number of minutiae.   

NIST will develop open‐source reference implementations for standardized quality components. 

3. Gray Box Feature Vector 

This  option  consists  of  a  subset  of  standardized  feature  vector  (clear  box)  appended  to a  vendor‐supplied 

proprietary feature  vector (black  box).  Vendors  can  opt for some  of the standardized  quality  components,  and 

extend that with their own proprietary quality components.  

3.1.2. Training a machine leaning algorithm  

Training looks for structure in the data and ultimately building a model to relate the response variable (e,g. performance 

as false non‐match rate, or  area‐under‐ROC‐curve) to the exploratory  variables (i.e.  features  or quality  components).  

We explore different multivariate statistical techniques to obtain the optimal model. Training could be customized to a 

comparison algorithm or generalized to a class of comparison algorithms.   

ELHAM.TABASSI[AT]NIST[DOT]GOV 
THE FUTURE OF NFIQ – MARCH 1, 2010 

Training will be performed by NIST.  NIST will return the detail information on the machine‐learning algorithm including 

trained weights to the vendor.  NIST will use some of its diverse set of finger images for training.  Avoiding over‐fitting 

problem  and  building  models  that  could  explain structures  of  unseen  data  is  an  important  and  difficult  problem  in 

training machine‐learning algorithms. Use  of a large and  diverse  set for  training  can mitigate this  problem.  NIST will 

calibrate quality scores either as part of  training or afterwards, and  therefore ensuring interoperability of  final quality 

scores. 

The  outcome  will  be  a  family  of  quality  algorithms  that  could  be  application‐independent  or  tuned  to  particular 

applications.  Interoperability  is  achieved  by  uniform  interpretation  of  quality  scores;  therefore,  it  expands  a 

marketplace of interoperable products. 

Figure 1 summarizes our proposed approach to NFIQ 2.0. 

3.2. Benefits 

The advantages of incorporating vendors’ provided features are a) improved efficiency (lower computation time) and b) 

better tuning of a quality algorithm to a specific application.  

The advantages of NIST developing multivariate  statistical  techniques  to linearly or non‐linearly combine  the  features 

into a quality score are a) ensuring  interoperability of  quality  scores, and b) use  of NIST’s  large and diverse data will 

result in development of more robust models and avoid the problem of over‐fitting to small data.  

NIST will calibrate quality scores either as part of training or afterwards, and therefore ensuring interoperability of final 

quality scores. 

Figure 1. NIST proposed NFIQ 2.0 

ELHAM.TABASSI[AT]NIST[DOT]GOV