We established teams at Lancaster University, University of Dundee and St John's College, Oxford, including experts in Human Anatomy, Computer Science, Mathematics, and Statistics. We have built a wider network, including imaging and biometric researchers and forensic practitioners.
Through the development of our outreach strategy, we established two large ground truth image datasets and a 3D dataset, including collection systems, infrastructure and databases. We developed a web-based application for data collection for our Large-Scale Citizen Science Dataset, which contains images from over 5000 individuals from both hands and various poses to allow for examination of our data extraction methodology. This set contains over 50,000 images, making it the largest hand image dataset in the world by a large margin. We have built a multi-camera rig for our High-Quality Dataset, which allows capture of colour and infrared images near simultaneously, allowing for comparison between the modalities, as well as mobile capture. This set has over 15,000 images from both hands of 650 volunteers in various poses. We built a further multi-camera photogrammetry rig for our 3-dimensional (3D) Hand Dataset, along with algorithms to reconstruct high-definition 3D data. This dataset contains detailed photographs and 3D reconstructions of the hands of 50 people.
To facilitate data capture and ensure public awareness of our work, we have developed and implemented plans for continuous engagement with periodic events, including press releases, television and radio interviews, newspaper articles, a blog and demonstrations of our work at expos and conferences, as well as numerous research articles and conference presentations.
A key objective was to develop the ability to extract key features from photographs of the hand, including superficial veins, knuckle and palmar creases, pigmentation, scars and lunules. We have developed new state-of-the-art (SOA) approaches for vein pattern tracing, extraction and mapping in two modalities, as well as crease and pigmentation extraction. We have developed localisation techniques to find and identify the hand as well as key regions from any image (regardless of scene, camera and quality) including knuckles and joints, punctate pigmentation, fingernails, and lunules. Our accuracy is world-leading and has on many occasions surpassed the SOA.
In addition to building ultra high-resolution 3D reconstructions from photogrammetry, we have developed new and world-leading techniques for achieving 3D reconstruction of the hand from single images, including simultaneous determination of the surface and texture. We have developed work in reposing to allow us to simulate changes in hand texture, allowing us to examine the impact of movement on our feature extraction algorithms.
We have developed white-box and black-box feature comparison methodology, including for punctate patterns (pigmentation and scars), curvilinear structures (skin creases), and graph structures (vein patterns and pigmentation clusters). Several studies have been conducted to investigate the relative contribution of different anatomical constructs, and we have developed methodology to determine the hierarchy of features and their respective contributions to identification. We have completed work in uncertainty estimation and its impact on identification, which is important for adoption of the work into practice.
We have published 12 publications in world-leading journals and conferences, including Computer Vision and Pattern Recognition (CVPR), ranked number 2 publication in the world according to Google Metrics, as well as the European Conference on Computer Vision (ECCV), Pattern Recognition, International Joint Conference on Biometrics (IJCB) and Sensors. We have demonstrated the work through demonstrations at major venues, including CVPR, ECCV, and the European Association of Biometrics.
