When age-progressed images are unreliable: The roles of external features and age range

Abstract

When children go missing for many years, investigators commission age-progressed images from forensic artists to depict an updated appearance. These images have anecdotal success, and systematic research has found they lead to accurate recognition rates comparable to outdated photos. The present study examines the reliability of age progressions of the same individuals created by different artists. Eight artists first generated age progressions of eight targets across three age ranges. Eighty-five participants then evaluated the similarity of these images against other images depicting the same targets progressed at the same age ranges, viewing either whole faces or faces with external features concealed. Similarities were highest over shorter age ranges and when external features were concealed. Implications drawn from theory and application are discussed.

A guided manual method for juvenile age progression using digital images

“…The reliability of age progression images has been explored previously (Erickson et al, 2016;Lampinen et al, 2015) by comparing depictions of the same individual created by different artists. In these studies the assessment of reliability was based on human perception and the authors concluded that the depictions varied in resemblance to the target.…”

“…Adobe Photoshop). The age progression technique varies between practitioners (Erickson et al, 2016) and some practitioners prefer to put more weight on quantifiable growth data, whilst others put more weight on the features of other family members (Taylor, 2000). In order to understand and produce a more accurate depiction, images of siblings and parents at the same age of the progression are often required to help artists to maintain a reliable likeness with biological resemblance (Lampinen et al, 2015).…”

“…Averaged facial template transparencies were produced in frontal view for every age from 3-18 years old, and in lateral view from 1-18 years old. These frontal templates could be used as a guide in age progression to estimate facial growth in frontal images of children, but these nonspecific growth templates are not ideal compared to gender-specific models (Erickson et al, 2016).…”

Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data

“…The absence of 3D facial models of missing persons that need to be age-progressed is another potential problem in real forensic scenarios, and methods of 3D face reconstruction from 2D photographs or images from video surveillance cameras have been intensively studied for many years [47–49]. In forensic practice, most age progressions are still produced by specialized artists, and the reliability of age progressions for the same individuals created by different artists can vary substantially [50]. It is important to develop more objective prediction methods that will be based on natural growth patterns.…”

“…According to Tome et al [63], the regions with the greatest discrimination power in facial recognition are the nose and forehead. Conversely, the external shape, which can conspicuously change during maturation, with weight gain or loss, or with changes in hairstyle, can affect identification accuracy [50] and may modify the representation of internal facial features in terms of face recognition [64].…”