Recently, implementing biometric technology for identification purposes has become an increasingly viable option due to costs decreasing rapidly and increased promotion of biometrics use in developing countries by global corporations and donors such as the World Bank (Kloppenburg & Ploeg, 2020, p.57).
What is Biometric Identification?
Biometric identification is a method that identifies individuals by automatically recognising a particular physical and/or behavioural characteristic such as fingerprints and iris patterns (Kloppenburg et. al., 2020, p.57).
There are 2 types of biometric methods:
- Physiological Biometrics – based on physiological characteristics such as fingerprints, hand geometry and retina patterns (Deane et. al., 1995, p.225)
- Behavioural Biometrics – based on behavioural aspects such as signature, voice, keystroke and point patterns (Deane et. al., 1995, p.225)
History of Biometrics
The need for trustworthy identification of people originated as early as the ancient times where the Middle and the Far East used bodily features such as height and weight to confirm the people’s identity (Otti, 2017, p.164). Fingerprint identification became adopted in New York State Prisons in 1903 to verify criminals’ identity which attributed to the development of the fingerprint analysis department of FBI in 1921 (Otti, 2017, p.164). However, automated fingerprint identification was not yet developed until the 1960s (Otti, 2017, p.164).
Aadhaar, India’s controversial biometric identification operation, was launched in 2009 and rolled out from 2014, intending to improve the monitoring of potential terrorists and decrease national criminal activity (Bhatia & Bhabha, 2017, pp.64-65). It is a voluntary identity acquisition that qualifies Indian citizens to receive benefits and use government and financial services with an Aadhaar card: an identity token holding a 12-digit number connected to the resident’s demographic and biometric information such as retina scans and fingerprints (Bhatia et al., 2017, pp.64-65).
However, the costs of utilising this method have arisen that outweigh its advantages which include fraud prevention and accessing benefits such as free school meals and fuel subsidies (Doshi, 2018). Increasing concerns about data privacy, data gathering accuracy, card connectivity issues and card connectivity access have threatened the viability of public acceptance. The biometrics’ reliability in India has been further undermined by existing cases where villagers were denied because of faulty card scanners and businesses stole client’s rations by falsely ‘claiming’ that the scanner was dysfunctional (Khera, 2018, p.340). Although signing up is optional, the Indian government has made “Aadhaar registration mandatory for access to many crucial government services” (Doshi, 2018), leaving marginalised communities and undocumented workers from Bangladesh and elsewhere who were not born in India without access (Biswas, 2018).
Seeing India’s current biometric identification scheme and its presented advantages and flaws, should Australia implement these systems?
Should Australia implement biometric identification methods?
Let’s discuss both perspectives behind implementing biometric identification methods.
‘The body does not lie’
Biometrics assumingly provide more secure identification and verification because of the certainty results from presumed unchangeable and unique nature of the individual biometric features. This contrasts tokens, cards and passwords that can be lost, copied, forged and shared (Jain & Ross, 2008, p.1).
Potential Verification ‘Invisibility’
Behavioural biometric methods in particular provide an unobtrusive and less intrusive method to verify users compared to physiological processes. It is only when a user’s behaviour becomes deviant that explicit verification prompts would become necessary (Deane et. al., 1995, p.226). Furthermore, the familiar process involved in behavioural biometric identification and the absence of designing and remembering passwords would enhance the user’s convenience and be less time-consuming to adapt to (Jain et. al., 2008, p.3).
A Surveillance Tool and its Negative Recognition Feature
Biometrics can be a valuable security measure as it provides the raw data of who-is-where (Kim, 1995, p. 212). In an organisational setting, it would result in a timely decision and adequate prevention of various forms of abuse and fraud through the automatic record and audit analysis of employee’s action patterns (Kim, 1995, pp.212-213).
This system can also prohibit impostors from claiming multiple benefits under different names through negative recognition. This is when a “system determines that a certain individual is enrolled in the system although the individual might deny it” (Jain et. al., 2008, p.3).
‘The body changes over time’
Although ‘the body does not lie’, ‘the body does change over time’. Individuals who acquire physical constraints or not distinctive enough traits for the system to recognise may be unable to enrol in a system (Pato & Millet, cited in Kloppenburg et. al., 2020, p.58). The system neglects the body’s fragility over time and how fingerprints can become worn especially for manual workers and how weight gains and losses, ageing, scarring and plastic surgery can alter an individual’s appearance (Kloppenburg et. al., 2020, p.62).
The normality assumption is also built into the equipment which sets a limited range of variation of human bodily features a system can cope with (Kloppenburg et. al., 2020, p.62). For example, cameras to scan faces may be aimed at a specific height or optimised for particular light and colour ranges and the accompanying face recognition software may have been wired to suit those that fall within a particular shade range of skin colour (Kloppenburg et. al., 2020, p.62). Consequently, many marginalised people such as people with disabilities and people of colour experiencing difficulties to enrol in a biometric system.
Error Tolerance (or Threshold) Setting
With all technology, there are technical errors which impact its ability to be widely accepted and relied upon. In biometric identification systems, 2 potential errors stem from its error tolerance (or threshold) setting.
- Type 1 Error: False Alarm Rate (FAR) – If the threshold is set to prevent impostors, some legitimate users may experience difficulties to gain access which results in a high FAR (Kim, 1995, p.207).
- Type 2 Error: Impostor Pass Rate (IPR) – If authorised users easily gain access, impostors may slip through the system with relative ease which increases the IPR (Kim, 1995, p.207).
To reduce type 1 error rates, it can only be achieved at the cost of increased type 2 error rates (Kim, 1995, p.207).
For biometric identification to be viable in Australia, the maintenance of the error tolerance setting must be carefully monitored to prevent fraud and inaccessibility to the system for authorised users.
Many have argued that implementing these identification schemes results in a loss of privacy due to the increased concern about the confidentiality of personal details provided to the system. If impostors obtain either the database with the templates or transmission of an individual’s biometrics due to the error tolerance setting loophole, they can impersonate that user, exposing that user to privacy violation (Kim, 1995, p.212). In the case that this occurs, genuine users are unable to be protected as it is difficult to invalidate fraudulent claims and easily alter a ‘biometric password’ (Kim, 1995, p.212).
Also, governments can manipulate the system for personal gain such as for population monitoring purposes (Kim, 1995, p.212). With the use of biometrics as surveillance tools, it poses risks to personal data protection as little transparency is provided with how biometric data is being used (Kim, 1995).
Implementing biometric identification systems can be a costly alternative to current identification methods. Biometric equipment for iris scanning and fingerprint imaging, although highly accurate and reliable, cost relatively higher than equipment required to verify an individual’s signature or typing patterns (Deane et. al., 1995, p.226). However, a recent increase in the adoption of biometric devices has made it more affordable where small businesses and even individuals can easily purchase them for business and personal security (Thakkar, 2020).
Despite biometric identification provides a secure alternative to verifying citizen identity and a surveillance tool to prevent fraud, there are various factors such as reliability and data privacy risks that deem it as unviable for the Australian context. For Australia to implement this system, a strong emphasis on earning the user’s trust through transparency about how the data is being collected and stored is of paramount importance to prevent Australian’s current hesitant acceptance and for successful system implementation (Deane et. al., 1995, p.229).
Hypertextual Article Reference List
Bhatia, A. & Bhabha, J. (2017). India’s Aadhaar scheme and the promise of inclusive social
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Biswas, S. (2018). Aadhaar: Is India’s biometric ID scheme hurting the poor?.
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Doshi, V. (2018). A security breach in India has left a billion people at risk of identity theft.
Frank D., Kate B., Ron H., & Doug M.. (1995). Perceived acceptability of biometric security
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Jain A.K., & Ross A. (2008). Introduction to Biometrics. In Jain A.K., Flynn P., Ross
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Khera, R. (2018). The Aadhaar debate: Where are the sociologists?. Contributions to Indian
Sociology, 52 (3), 336–342. doi: 10.1177/0069966718787029.
Kim, H. (1995). Biometrics, is it a viable proposition for identity authentication and
access control?. Computers & Security, 14 (3), 205-214. ISSN 0167-4048.
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Enactment of Human Bodily Differences. Science as Culture, 29 (1), 57-76, doi: 10.1080/09505431.2018.1519534.
Otti, C. (2017). THE PAST, PRESENT AND FUTURE OF BIOMETRICS. Annals of the
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Thakkar, D. (2020). Biometric Devices: Cost, Types and Comparative Analysis.
Retrieved from: https://www.bayometric.com/biometric-devices-cost/
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Retrieved from: https://www.flickr.com/photos/157522514@N03/44925815185/in/photolist-2brWxEr-W7xnwa-R6yQya-7jb9c-A34Xmg-227vK3C-aq8Ddf-2h4hy2L-8dr88T-8duoas-8draTB-LfEu6Q-JDiGRD-JB62aG-LjgJtL-GArszq-nf2i45-AyyRaX-2iWDRaX-2j2uLX3-QuTS8D-2gRmWSV-Za2hQ1-2h2g7ki-2h6Jvkq-2hkEw2d-2h2gHMB-EBMAPH-pPvVZS-oeTstf-JB62h5-JB62d7-JB62gd-PV7iA1-SuhmRc-C9H38o-6j2Dog-C9Q23n-Jt15WK-6Wvwuz-mSjmEL-2jY4CPU-6iVcuF-2jY4CNr-2jXYXyD-pydCcV-wYbjX3-255Y2jw-x9LVD4-22mBFna