Biometric Recognition of the Hand in Uncontrolled Images

Overview

Incidents of abuse material found online have massively increased in the last decade, presenting an epidemic that law enforcement agencies are struggling to keep up with. In image or video documented crime perpetrators often take steps to maintain anonymity, including hiding their faces; biometric analysis of this type of content is one way to identify the people involved. Hands and forearms are more often visible and contain many unique features such as hand geometry, palm and knuckle prints, under-skin vein patterns, androgenic hair patterns, and marks such as scars, freckles, and tattoos. The key aim of this project is to investigate the usability of these features for offender identification, and develop effective methods of extracting them.

There are research gaps in the application of hand biometric feature extraction in uncontrolled images, where there is a solid basis of research in controlled images. A method designed for a forensic investigation context has several extra requirements: detection of features before extraction, invariance to different image conditions, a matching process that evaluates within-source and between-source variance, and being explainable to a layperson - a requirement of providing the algorithm's output as evidence. Uncontrolled hand image data is scarce so creation of further datasets will be necessary to develop these methods. Requirements for this data will be determined by an initial analysis of pre-existing data and the available features.

The project will consider the history of more established biometrics used in forensic investigation (fingerprints, DNA, facial recognition) with regards to data privacy, use and mis-use by law enforcement, and admissibility of evidence. This will be done with the aim of better designing methods from the start, avoiding the same pitfalls encountered with biometric evidence in the past.

Research Questions

What methods can be used to extract hand biometric information consistently and reliably, from the uncontrolled conditions often found in criminal material?
How accurate will recognition be when performed on these extracted features?
Which of these features, or combinations of these feature, are best for automatic recognition?

We will collaborate with over 40 partners drawn from across FMCG and Food; Creative Industries; Health and Wellbeing; Smart Mobility; Finance; Enabling technologies; and Policy, Law and Society. These will benefit from engagement with our CDT through the following established mechanisms:

- Training multi-disciplinary leaders. Our partners will benefit from being able to recruit highly skilled individuals who are able to work across technologies, methods and sectors and in multi-disciplinary teams. We will deliver at least 65 skilled PhD graduates into the Digital Economy.

- Internships. Each Horizon student undertakes at least one industry internship or exchange at an external partner. These internships have a benefit to the student in developing their appreciation of the relevance of their PhD to the external societal and industrial context, and have a benefit to the external partner through engagement with our students and their multidisciplinary skill sets combined with an ability to help innovate new ideas and approaches with minimal long-term risk. Internships are a compulsory part of our programme, taking place in the summer of the first year. We will deliver at least 65 internships with partners.

- Industry-led challenge projects. Each student participates in an industry-led group project in their second year. Our partners benefit from being able to commission focused research projects to help them answer a challenge that they could not normally fund from their core resources. We will deliver at least 15 such projects (3 a year) throughout the lifetime of the CDT.

- Industry-relevant PhD projects. Each student delivers a PhD thesis project in collaboration with at least one external partner who benefits from being able to engage in longer-term and deeper research that they would not normally be able to undertake, especially for those who do not have their own dedicated R&D labs. We will deliver at least 65 such PhDs over the lifetime of this CDT renewal.

- Public engagement. All students receive training in public engagement and learn to communicate their findings through press releases, media coverage.

This proposal introduces two new impact channels in order to further the impact of our students' work and help widen our network of partners.

- The Horizon Impact Fund. Final year students can apply for support to undertake short impact projects. This benefits industry partners, public and third sector partners, academic partners and the wider public benefit from targeted activities that deepen the impact of individual students' PhD work. This will support activities such as developing plans for spin-outs and commercialization; establishing an IP position; preparing and documenting open-source software or datasets; and developing tourable public experiences.

- ORBIT as an impact partner for RRI. Students will embed findings and methods for Responsible Research Innovation into the national training programme that is delivered by ORBIT, the Observatory for Responsible Research and Innovation in ICT (www.orbit-rri.org). Through our direct partnership with ORBIT all Horizon CDT students will be encouraged to write up their experience of RRI as contributions to ORBIT so as to ensure that their PhD research will not only gain visibility but also inform future RRI training and education. PhD projects that are predominantly in the area of RRI are expected to contribute to new training modules, online tools or other ORBIT services.