Datasets for Face Recognition 

at a Distance (FRAD)

Brendan F. Klare, Ph.D.

Visual Analytics Lab @ Noblis

brendan.klare@noblis.org
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Importance of Datasets

 Algorithm = f (data)

• Biometrics algorithms are a function of the 

data that they are trained on

• More data  better algorithms

 In order to better understand biometric 

capabilities in operational settings, data is 

needed that best replicates the intended 

scenario

 As new problems and opportunities 

emerge that can leverage biometrics, it is 

imperative to have analogous datasets 

available to train and evaluate such 

algorithms 
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Understanding Face Recognition at a Distance

 Identifying persons at a distance is helpful in law enforcement, defense, and 

intelligence scenarios

 Provides an understanding of what persons, and how many persons, are 

present at a location

 Two primary operational paradigms:

• Watch list identification

• Re-identification

 Several challenges may exist:

• Low resolution 

• Motion blur

• Heterogeneous matching 

• Atmospheric noise

• PIE variations

• Sensors and illuminators 

Distant 

image

Watch-list 

image Match

Distant 

image

Distant 

image Match

Watch-list identification:

Re-identification:
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Heterogeneous Face Recognition

Frontal photograph exists 

for majority of the population

Matching non-photograph face images (probe images) to large databases of frontal 

photographs (gallery images) is called heterogeneous face recognition (HFR)

In many scenarios only alternate image 

modalities or can be captured
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Understand Infrared Spectrum

 FRAD often requires infrared capture due to nighttime conditions

 Active sensing bands (requires illumination):

• Visible

• NIR (Near infrared)

• SWIR (short wave infrared)

 Passive sensing bands (does not require illumination):

• MWIR (medium wave infrared)

• LWIR (long wave infrared)

Image from: 

T. Bourlai, B. Cukic, Multi-Spectral Face Recognition: Identification of People in Difficult 

Environments, in Proc. ISI, 2012, pp. 196-201.
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Face Appearance Across the IR Spectrum

NIR SWIR MWIR

0.4 0.7 1.0 2.5 3.0 5.0

Visible

Wavelength 

(micrometers)

Infrared type

NIR = Near Infrared

SWIR = Short Wave Infrared

MWIR = Medium Wave Infrared (or thermal)
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Long Distance Heterogeneous Face Database (LDHF)

 Face recognition database from Korea University containing both visible 

and infrared imagery from 100 subjects

 Each subject has the following imagery:

• At a distance:

– Images captured at 60m, 100m, and 150m outdoors

– Both visible and near-infrared images collected

– Both daytime and nighttime collection

• Reference image:

– Visible and near infrared image captures at 1m indoors (analogous to id card 

image)

D. Kang, H. Han, A. K. Jain, and S. W. Lee, "Nighttime Face Recognition at Large Standoff: Cross-Distance 

and Cross-Spectral Matching", Pattern Recognition, Vol. 47, No. 12, pp. 3750-3766, December 2014.

http://biolab.korea.ac.kr/database/index.html
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WVU FRAD Data

 Datasets used in research by WVU [1] and partially collected by the West 

Virginia High Technology Consortium (WVHTC) Foundation contained at 

distance imagery

 Relevant datasets from [1]:

• SWIR at a distance:

– SWIR DB2 – 50 subjects with SWIR images collected at 50m and 106m with 

controlled pose variation and minor facial expression variations

– SWIR DB3 – 16 subjects in an uncontrolled settings with distances ranging 

from 60 to 400 meters

• NIR at a distance:

– 103 subjects with NIR captured at 30, 60, 90, and 120 meters

– One visible video per subject was collected

T. Bourlai, B. Cukic, Multi-Spectral Face Recognition: Identification of People in Difficult 

Environments, in Proc. ISI, 2012, pp. 196-201.
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UCCS Large Scale Unconstrained Open Set Face Database

 Fully covert FRAD database, with images 

captured in public environments without 

subject cooperation 

 The most covert dataset available to date

 Details:

• Contains 6,337 visible images from 308 

subjects

• Images captured at a distance of roughly 

100m 

• Dataset is ideal for re-identification scenario

Sapkota, Archana, and Terrance E. Boult. "Large scale unconstrained open set face database." Biometrics: Theory, 

Applications and Systems (BTAS), 2013 IEEE Sixth International Conference on. IEEE, 2013.
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UMD Remote Face Database

 At a distance images captured in an unconstrained environment from 

cooperating subjects 

 Details:

• Contains 2,106 visible face images from 17 subjects captured between 5m and 

250m

• Images labeled based on clarity, occlusion, and illumination

Ni, Jie, and Rama Chellappa. "Evaluation of state-of-the-art algorithms for remote face recognition." Image Processing 

(ICIP), 2010 17th IEEE International Conference on. IEEE, 2010.
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Pinellas County Sheriff’s Office (PCSO)

 PCSO has provided the research community many unprecedented datasets 

 An FRAD dataset was collected previously collected to conduct accuracy 

experiments using at a distance images as a query against a database of 

1.5 million controlled capture images

 Details:

• 50 subjects total

• For each subject the following images were collected:

– 1m controlled capture “mugshot” style image

– Indoor at a distance of 50, 75, and 100 yards

– Outdoor at a distance of 50, 75, 100, 125, 150, and 200 yards
Thank you!