Ching Yiu Jessica Liu’s research while affiliated with Liverpool John Moores University and other places

Ramesses II was one of the most important Pharaohs to have presided over Egypt during the New Kingdom period. In 2023 researchers Wilkinson, Saleem, Liu and Roughley produced two digital 3D facial depictions showing Ramesses II at different ages: one around the age-at-death at 90 years old and the other, an ageregression at approximately 45 years old, based CT scans of his mummified remains, photographs, and historical information. The presence of two 3D facial depictions of one ancient individual at different ages affords an opportunity to show how Ramesses II might have looked during key moments of his lifetime and just prior to death. This paper describes the workflow adopted to add realistic textures to the facial depictions, and to use a morph-based animation to represent Ramesses II ageing from 45 to 90 years old.

Facial reconstruction is a technique that can be used to estimate individual faces from human skulls. The presentation of 3D facial reconstructions as photo-realistic depictions of people from the past to public audiences varies widely due to differing methods, the artists' CGI skillset, and access to VFX software required to generate plausible faces.This chapter describes three digital methods for the addition of realistic textures to 3D facial reconstructions; a 2D photo-composite method, a 3D digital painting and rendering method, and a previously undescribed hybrid 2.5D method.These methods are compared and discussed in relation to artistic proficiency, morphological accuracy and practitioner bias.

Predicting the possible age-related changes to a child's face, age progression methods modify the shape, colour and texture of a facial image while retaining the identity of the individual. However, the techniques vary between different practitioners. This study combines different age progression techniques for juvenile subjects, various researches based on longitudinal radiographic data; physical anthropometric measurements of the head and face; and digital image measurements in pixels. Utilising 12 anthropometric measurements of the face, this study documents a new workflow for digital manual age progression. An inter-observer error study (n = 5) included the comparison of two age progressions of the same individual at different ages. The proposed age progression method recorded satisfactory levels of repeatability based on the 12 anthropometric measurements. Seven measurements achieved an error below 8.60%. Facial anthropometric measurements involving the nasion (n) and trichion (tr) showed the most inconsistency (14-34% difference between the practitioners). Overall, the horizontal measurements were more accurate than the vertical measurements. The age progression images were compared using a manual morphological method and machine-based face recognition. The confidence scores generated by the three different facial recognition APIs suggested the performance of any age progression not only varies between practitioners, but also between the Facial recognition systems. The suggested new workflow was able to guide the positioning of the facial features, but the process of age progression remains dependant on artistic interpretation.

Machine-based facial recognition could help law enforcement and other organisations to match juvenile faces more efficiently. It is especially important when dealing with indecent images of children to minimise the workload, and deal with moral and stamina challenges related to human recognition. With growth related changes, juvenile face recognition is challenging. The challenge not only relates to the growth of the child’s face, but also to face recognition in the wild with unconstrained images. The aim of the study was to evaluate how different conditions (i.e. black and white, cropped, blur and resolution reduction) can affect machine-based facial recognition of juvenile age progression. The study used three off-the-shelf facial recognition algorithms (Microsoft Face API, Amazon Rekognition, and Face++) and compared the original images and the age progression images under the four image conditions against an older image of the child. The results showed a decrease in facial similarity with an increased age gap, in comparison to Microsoft; Amazon and Face++ showed higher confidence scores and are more resilient to a change in image condition. The image condition ‘black and white’ and ‘cropped’ had a negative effect across all three APIs. The relationship between age progression images and the younger original image was explored. The results suggest manual age progression images are no more useful than the original image for facial identification of missing children, and Amazon and Face++ performed better with the original image.