The use of biometric-based digital IDs is growing worldwide. This article analyses the latest developments in digital identification, the likely next steps, and how to deal with the core concerns this evolution raises: ensuring the security and ethical use of the personal data involved.
Biometric data injection attacks present a new threat to digital identities, digital wallets and remote identity proofing, which the digital ID community must counter if they are to prevent potentially massive identity fraud attempts.
Tech innovation experts Dr Rajeev Kumar and Dr Alka Agrawal show how blockchain technology can be integrated with biometric apps in the healthcare sector, to protect highly sensitive patient data from cyber-attacks.
The European Union has an ambitious plan to establish a digital single market, with wide-ranging data sharing built on biometric-based digital IDs. But how easy will this be to deliver, and what is the likely impact on European citizens and companies?
As businesses move increasingly online, it's critical that they get authentication and authorisation right in order to preserve trust, says Jasmit Sagoo of Auth0.
Hand biometric authentication has come to the fore during the Covid crisis, as a hygienic, contactless way to ID people. But what's the future for hand recognition post-pandemic, and how does it stack up against rival biometric modes? We speak to Bernard Garcia, CEO of hand identity specialist nVIAsoft.
OneSpan research scientist Ismini Psychoula outlines the many types of bias that can exist in face and other biometric systems – and how to counter them.
Although facial recognition systems are improving all the time, they are still vulnerable to ‘morphing’ attacks, say Christian Rathgeb, Johannes Merkle, Ulrich Scherhag and Christoph Busch. They examine the challenges in detecting and defeating these latest threats.
E-signatures are increasingly being used in online business, but they still present security risks. Mo Sahib of Borderless Security and FilesDNA explains how the latest intelligent biometric systems can solve this.
When it comes to online verification, passive liveness detection (PLD) offers a superior user experience because it asks nothing of the customer. But not all PLD systems have the features needed to maximise their performance, says Jan Lunter of Innovatrics.
From algo training to setting testing standards, Stephen Ritter of Mitek outlines the main actions users and developers can take to eradicate facial recognition bias.
Fingerprint ID has come a long way since around 100 years ago, when police forces first started to capture fingerprints using ink and paper. This article takes a deep-dive into the development of fingerprint capture – in particular the innovations made over the past 25 years, and what these mean for the future of this technology in key areas like mobile phone authentication.
In recent years, biometric systems have moved from protecting only mission-critical government facilities to being used by virtually any organisation that can afford access control, including hospitals, corporate offices, schools and small businesses. Across these sectors, end users typically choose between the top three biometric technologies – iris, facial and fingerprint recognition. However, over the past 18 months, the Covid-19 pandemic has upended the biometrics market, with users rapidly moving toward touchless solutions as part of an overall effort to slow the spread of the deadly virus.
Covid-19 has halted many things we once thought of as normal. At the beginning of the pandemic, countries quickly closed down their borders in an attempt to staunch the free flow of infections. Given the limited information about the virus at the time, these restrictions were a natural response; after all, health authorities could often trace initial infections in a country back to a handful of international travellers.
Behavioural biometrics – much like physical biometrics – is becoming an increasingly prevalent way for financial institutions worldwide to authenticate and verify their customers. At a time when a staggering 15 billion sets of stolen usernames and passwords – acquired through hundreds of thousands of data breaches – are circulating the dark web 1 , behavioural biometric analysis promises to replace systems relying on personally identifiable information (PII) with a more secure and user-friendly alternative.
In recent weeks, the pressure on governments worldwide to ban the use of live facial recognition (LFR) technology has reached new levels.
The Covid-19 pandemic has accelerated digital transformation across all industries, enabling many businesses to carry on trading while streamlining processes. But perhaps not surprisingly, this move to digital has been accompanied by a rise in cyber-crime and fraud. This in turn has accelerated the search for technology that can help improve the security of digital ID verification, while enhancing the experience for users.
Biometric safeguards that let people use their face as their password are not new, and like anti-virus software they must be constantly updated and enhanced. Crucially, the way that computers read nodal points in order to generate faceprints can and should continually be improved. It's one thing for a computer to read a snapshot of a face and compare it to a stored image, it's another thing entirely for a computer to determine if the information being fed to it is coming from the actual live version of the person whom they're claiming to be.
Biometrics has transformed global payments and has quickly become the go-to technology to provide secure and convenient identity verification (proofing), customer authentication, transaction authorisation and support for fraud detection.
Financial institutions need to pay special attention to the pro-biometrics sentiment now established among consumers. When a recent Credit Union Times study asked 9,000 consumers worldwide how they perceived a range of authentication methods in terms of security, for the first time in years passwords were not even ranked as among the top three 1 . Instead, a majority of consumers said they preferred biometrics such as fingerprint or face recognition or more passive behavioural biometrics that track how users type and swipe on their mobile device.
