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© UNICEF/UN0298796/Ramasomanana
Biometrics for Children
Key questions, challenges and responses
Karen Carter - Admin Data Specialist Nicola Richards - Consultant
October 2019
Shaping a response - biometrics for children
• How should biometrics be used in our programs?
• How do we support member states to make good decisions on biometrics for children?
• Caveat – most of the potential uses of biometrics in areas of interest to UNICEF, are actually about biometric enrollment of adults, i.e.
• Teacher incentive payments
• Cash payments for households and families (primarily in humanitarian settings) - enrollment and facilitation of payments (incl. data sharing with other agencies)
• Use of biometric-based national ID of parents in birth registration
• Verification of data from “trusted informants” - such as community nurses and local leaders to improve community access to birth registration
UNICEF | Data for Children 2
Understanding potential use cases for children
Current uses within the UN system
Identity Management of refugee childrenIncluding: Identification of geographic movements, Protection of identity against exploitation,
Use as a tool for protection (triggering child protection interventions)
UNHCR
Beneficiary registration – including verification of children in the household WFP, IOM
(CT guidance re sharing biometrics across borders for intelligence purposes) UN CCT
Potential uses that have been proposed to UNICEF
School attendance Small pilots
Vaccine tracking and health record linkage Under testing by other
development agencies
Continuity of services – particularly in child protection for children on the move Preliminary planning
Use of biometrics to improve linkages between birth registration (CRVS) and
national ID processes, age verification processes (i.e. to prevent child marriage) etc.
National governments
Prevention of child trafficking (A few national schemes
exist – limited assessment)3
Key concerns and questions
• Do biometrics systems ACTUALLY add value to outcomes for children? Is this greater than having another form of unique ID?
• What are the specific risks for children?
• How do we make sure issues of exclusion and equity for all children are not made worse by the use of biometric technology in our programs?
• Does the technology actually work? How well does it work over time?
• How do we work with partners in this space?
• How do we ensure technology development puts children’s rights first?
UNICEF | Data for Children 4
Key pieces of work
• Development of internal guidance for UNICEF programs
• Faces, fingerprint, and feet: Released August 2019
• Literature review of existing knowledge – in collaboration with the WB ID4D team
• Available research (publicly available)
• Technology performance
• Collaboration with UN partners – working group on Biometrics under the Legal Identity Expert Group
https://data.unicef.org/resources/biometrics/
UNICEF | Data for Children 5
Faces, Fingerprints & Feet: Guidance document
• Background
• Introduction to key concepts
• Benefits, risks and concerns
• Practical approaches in assessing the use of biometric technologies
• 10 key questions – including background to each
• Flowchart
• Planning template
• Implementation
• Next steps and where to go for more help
UNICEF | Data for Children 6
© UNICEF/UN050570/Mukwazhi
Should I consider using biometrics?
1. Does the use of biometric technology add value to the program?
2. Is there an established legal basis for collecting, receiving, or sharing biometric data?
3. Is the biometric technology appropriate for the target age group(s)?
4. Is the biometric technology accurate and reliable for the proposed project use?
5. Is the technology suitable for the setting?
6. How acceptable are biometrics among the target community?
7. Could the introduction of biometrics potentially exclude children or families from services or protection?
8. Can the data be appropriately protected at all stages from collection (or receipt) through to destruction?
9. Can data privacy and protection concerns be appropriately managed?
10. Are potential partners and implementing agencies equally committed to data protection and privacy measures?
8
Q4: Appropriate for the intended use
• Identification or verification?
• Performance measures vary significantly by technology and type of system (one study for example with facial recognition matched every child with every other child in the system)
• How accurate do we need the recognition to be?
• Food distribution, vaccine administration vs broader health records, vs ID registration, vs attendance, vs trafficking
• How long do we need to be able to identify a child for?
• The impact of ageing is largely unknown for most biometrics for children, or have demonstrated problematic findings
• How big/ dispersed is the population we are targeting?
• Can the process be managed locally or is greater processing power needed
• How secure is the infrastructure – locally and in the field
UNICEF | Data for Children 9
User acceptance will vary with case and context
Many things may affect acceptability:
• Trust in the implementing partner
• Level of risk associated with the trait being used
• Intrusiveness of the enrollment process
• Consent processes and engagement
• Are there sensitivities in the general community or specific groups towards biometrics?
• Is there potential for exclusion?
Q6: Acceptability
Specific risks for children
• Data protection and privacy
• Exclusion through system design or technology limitations and failures
• Unintended uses of linked data
Children need special consideration:
• Potential for exclusion as failures or errors are more likely to occur in children
• Children lack the agency or opportunity to be involved in making important decisions about their participation in services, or the knowledge and understanding required to make informed decisions about the processing of their own personal data
• Children are at the forefront of the ‘big data’ revolution, and this increases their likelihood of being exposed to lifelong data risks. More data will be collected on children over their lifetime than ever before, and the future use, applications and impact of this data on their lives is unpredictable
• Due to the permanence of biometric traits, they have the potential for harm that cannot be easily fixed or adjusted
UNICEF | Data for Children 11
Lit review - technologies for children
PERFORMANCE BY TRAIT
• What evidence is publicly available
• Performance results
• Ageing and implementation issues
Preliminary findings only:
UNICEF | Data for Children 12
Reported performance * of select biometric traits when used with children aged 0–4 years
13
Biometric feature Facial Fingerprint Iris Palmprint Footprint Earprint RetinaHand
geometryVein pattern DNA
Unique/distinct
The ability to adequately discriminate between individuals of an
entire population based on the particular trait
L H H H H L H M M H
Permanent
How persistent an individual’s biometric trait is over time with respect to the application and the matching algorithm used
LM MH H MH MH MH MH M M H
Universal
Every individual in the target population should possess the traitH MH H H MH H H M M H
Measurable
The system should be able to acquire and digitize the trait
without undue inconvenience to the user
H M M L LM H L H M L
Performance
Recognition accuracy (true accepts and false rejects) in terms of
computational resources required to achieve that level of
accuracy, and system throughput (number of transactions that
can be processed per unit of time)
LM MH H M H M H M M H
Acceptability
If there are community or family concerns regarding capturing or
recording a child’s traits in the system, there is low acceptability H MH L MH MH MH L M M L
Resistance to circumvention
The trait should be difficult to imitate or obscure (for example,
using a silicon finger or wearing a hat to hide facial features)
L H H M M L H M H H
Ease of use with children
M
Stare
towards
camera with
neutral
expression
M
Need
assistance by
operator to
hold finger
on sensor
L
Eyes must be
open and
stare
towards
camera
L
Open fist and
allow
operator to
apply
pressure
L
Remove
shoes and
socks, allow
operator to
apply
pressure
M
Need head
held still in
one position
L
Eyes must be
open and
stare
towards
camera
M
Need
assistance by
operator to
hold hand
over sensor
M
Need
assistance by
operator to
hold hand
over sensor
L
Depending
on method,
may require
blood sample
or swab of
saliva
Source: adapted from Jain, Ross & Nandakumar, 2011; Jain, et al, 2015; Kotzerke, 2014; van Greunen, 2016; Dinkar & Sambyal, 2012; Moolla, 2019
L = low; LM = low-to-medium; M = medium; MH = medium-to-high; H = high
* Scores were allocated based on subjective reviews from six previous research articles; a single score (i.e. ‘M’) represents all authors’ agreement on the performance level, while a range (‘LM’) represents the different scores provided by different authors.
Published data - Fingerprints
UNICEF | Data for Children 14
Author Location Subjects (N) Age groupVerification
(TAR %)
Identification
(Rank-1 %)
Moolla, 2019 NR NR 6 weeks – 5 months NR NR
Saggese et al, 2019*
Aronoff-Spencer, 2019
Mexico 325 0–3 days 89.00 91.68
67 4–30 days 99.00 99.81
108 >30 days – 5 months >99.00 99.75
Engelsma et al, 2019 India 194 0–3 months 66.70 – 90.20 NR
Galbally, Haraksim & Beslay, 2018* Portugal 1,600 0–4 years 66** NR
5–12 years 95** NR
NR 13–17 years 98** NR
Basak et al, 2017 India 106 18 months – 4 years 36.42 – 99.28 NR
Jain et al, 2017* India 309 6–12 months 95.00 NR
12–60 months 99.50 NR
6–12 months 98.90 100.00
12–60 months 100.00 100.00
Jain et al, 2016 India 66 ≤4 weeks 43.43 38.44
>4 weeks – 6 months 79.92 73.98
Jain et al, 2015 India 206 0–4 years 89.92 – 96.88 83.98 – 99.03
Jain, Cao & Arora 2014 USA 20 0–4 years 62.25 – 95.04 46.37 – 95.52
Benin 70 30.24 – 64.27 20.00 – 67.14
Uhl & Wild, 2009 Austria 301 3–18 years NR NR
Table 9: Studies examining fingerprint technology for use with children (0–17 years)
Published data - Fingerprints
15
Key:
Blue circle = identification
Purple circle = verification
Red circle = verification (adults)
Transparent circle
No data provided on
sample size
Small circle
0–99 participants
Medium circle
100–249 participants
Large circle
250+ participants
Figure 3: Accuracy (TAR/Rank-1) of fingerprint recognition technology (highest level achieved) by age group and sample size
92100 100
34
100
75
43
96100
0–3
da
ys
4–3
0 d
ays
>3
0 d
ays
–5 m
on
ths
0–6
mo
nth
s
6–1
2 m
on
ths
0–4
ye
ars
0–4
ye
ars
0–4
ye
ars
1–4
ye
ars
0
20
40
60
80
100
120
Re
cog
nit
ion
acc
ura
cy (
%)
Study age range
Identification
Summary of results - fingerprints
• There are few published large-scale feasibility studies regarding the use of fingerprint technology for children, with a notable gap in the evidence on the long-term reliability of the technology
- This is despite the widespread roll-out of the technology at population level
• Clear findings re. the difficulty in capturing high-quality images for use in automated recognition, resulting from both the smaller size of children’s fingers, and limitations of the technology itself
• Recognition accuracy among children (0–4 years) shows the greatest variability, and as few studies disaggregate their results by more granular age groups, it is difficult to assess the variability in accuracy by specific age or the proportion of younger to older children within each study
• Verification accuracy for older children (5–12 years) and adolescents (13–17 years), while high (95% and 98% respectively), is derived from only one study
• The limited evidence on longitudinal performance indicates that ageing is a significant challenge for recognition, with a 2017 study achieving 10% for verification and 31% for identification among infants (0–5 months) only six months after enrolment
UNICEF | Data for Children 16
Where does that leave us?
• Biometrics for under 5’s are largely experimental. Until the body of evidence is increased, these projects should be treated as research – with appropriate research controls
• Above age 5 – fingerprints and iris scans appear to be effective – but this needs to be backed up with verifiable data to support the proposed technology. There is a need for much better understanding of:
• Ageing and longitudinal performance over extended periods
• Performance by user characteristics apart from age (gender, ethnicity, disability, etc.) – with the limited evidence from adults showing important differences in accuracy
• Implementation issues – in particular, interaction with the sensor
• Above ~ 15, performance supports the use of biometrics with results similar to adults
• The risks to children are not simply the same data privacy and protection issues that face adults: the risks require special consideration
• IMPACT of biometric programs, and the benefit must be weighed against both alternative approaches and risks. Critical need for evidence of benefit.
UNICEF | Data for Children 17
Areas for further research
• There remains very little independently verifiable evidence on the performance of biometric traits with children
• Population-level analysis of both recognition accuracy and impact from the deployment of biometric systems with children
• Increased understanding of the attitudes and acceptability around biometrics is required, especially amongst adolescents, and how to engage children in the process
• An understanding of the data risks and experiences over the life course of an individual is needed, especially as children are at the forefront of the ‘big data’ revolution, increasing their likelihood of being exposed to lifelong data risks, including privacy and security concerns
• The role of algorithms, especially if tied to legal identity, also needs more research and consideration
UNICEF | Data for Children 18
https://data.unicef.org/resources/biometrics/
Thank you
Contact: Karen Carter – Admin Data Specialist
kcarter@unicef.org
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