Senior Research Associate in Image Processing/Computer Vision and Machine Learning: In search of uniqueness â harnessing anatomical hand variation (H-unique) (A2560-R)

As part of the H-Unique project we are seeking to appoint a senior research associate in Image Processing/Computer Vision and Machine Learning. The primary aim of the H-Unique project is the successful analysis and interpretation of anatomical variation in images of the human hand. This will be achieved by developing new image processing/computer vision methods to extract key features from human hand images (e.g. vein pattern, skin knuckle creases, tattoos, pigmentation pattern) in a way that is robust to changes in viewpoint, illumination, background, etc. The project will be successful if no two hands can be found to be identical, implying “uniqueness”.

This exciting research opportunity has arisen directly from the ground-breaking research undertaken by Prof Dame Sue Black in relation to the forensic identification of individuals from images of their anatomy captured primarily within IIOC (indecent images of children). Assessment of the evidential robustness of hand identification in the courtroom requires that the degree of “uniqueness” in the human hand be assessed through large volume image analysis. The research opens up the opportunity to develop new and exciting biometric capabilities that have a wide range of real-world applications, from security access through to border control whilst assisting the investigation of serious and organised crime on a global level.

H-unique is a five year, €2.5M programme of research and will be the first multimodal automated interrogation of visible hand anatomy, through analysis and interpretation of human variation. It will be an interdisciplinary project, supported by anatomists, anthropologists, geneticists, bioinformaticians, image analysts and computer scientists. We will investigate inherent and acquired variation in search of uniqueness, as the hand retains and displays a multiplicity of anatomical variants formed by different aetiologies (genetics, development, environment, accident etc). Hard biometrics, such as fingerprints, are well understood and some soft biometrics are gaining traction within both biometric and forensic domains (e.g. superficial vein pattern, skin crease pattern, morphometry, scars, tattoos and pigmentation pattern). A combinatorial approach of soft and hard biometrics has not previously been attempted from images of the hand. We will pioneer the development of new methods that will release the full extent of variation locked within the visible anatomy of the human hand and reconstruct its discriminatory profile as a retro-engineered multimodal biometric. A significant step change is required in the science to both reliably and repeatably extract and compare anatomical information from large numbers of images especially when the hand is not in a standard position or when either the resolution or lighting in the image is not ideal. Large datasets are vital for this work to be legally admissible. Through citizen engagement with science, this research will collect images from over 5,000 participants, creating an active, open source, ground-truth dataset. It will examine and address the effects of variable image conditions on data extraction and will design algorithms that permit auto-pattern searching across large numbers of stored images of variable quality. This will provide a major novel breakthrough in the study of anatomical variation, with wide ranging, interdisciplinary and transdisciplinary impact.

We invite applications from enthusiastic individuals who have a PhD or equivalent experience in a relevant discipline such as Computer Science or Electrical Engineering. You must be able to demonstrate a research background in the area of image processing, computer vision, and/or deep learning. Familiarity with biometric image analysis methods and machine learning/deep learning frameworks is not essential but will put you at an advantage. We will also value highly your ability to learn rapidly and to adapt to new technologies beyond your current skills and expertise. For more details, please see the Job Description/Person Specification for this position.

The School of Computing and Communications offers a highly inclusive and stimulating environment for career development, and you will be exposed to a range of further opportunities over the course of this post. We are committed to family-friendly and flexible working policies on an individual basis, as well as the Athena SWAN Charter, which recognises and celebrates good employment practice undertaken to address gender equality in higher education and research.

This Senior Research Associate position is being offered on a fixed-term basis until 31 December 2023. For further information or an informal discussion please contact Professor Plamen Angelov (Email: p.angelov@lancaster.ac.uk) or Dr Hossein Rahmani (Email: h.rahmani@lancaster.ac.uk)

Lancaster University - ensuring equality of opportunity and celebrating diversity

Closing Date: 25 Oct 2019
Category: Research