Whole-face procedures for recovering facial images from memory

The role of visual imagery in face recognition and the construction of facial composites. Evidence from Aphantasia

People with aphantasia have a markedly impaired ability to form visual images in the mind's eye. Here, by testing people with and without aphantasia, we examine the relationship between visual imagery and face processing. We show that aphantasics have weaker face recognition than people with visual imagery, using both self-report (Prosopagnosia Index) and behavioural measures (Cambridge Face Memory Test). However, aphantasics nonetheless have a fully intact ability to construct facial composites from memory (i.e., composites produced using EFIT6 by aphantasics and imagers were rated as equally accurate in terms of their resemblance to a target face). Additionally, we show that aphantasics were less able than imagers to see the resemblance between composites and a target face, suggestive of potential issues with face matching (perception). Finally, we show that holistic and featural methods of composite construction using EFIT6 produce equally accurate composites. Our results suggest that face recognition, but not face composite construction, is facilitated by the ability to represent visual properties as 'pictures in the mind'. Our findings have implications for the study of aphantasia, and also for forensic settings, where face composite systems are commonly used to aid criminal investigations.

Inspirational Adversarial Image Generation

The task of image generation started receiving some attention from artists and designers, providing inspiration for new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools. In this work, we propose a simple strategy to inspire creators with new generations learned from a dataset of their choice, while providing some control over the output. We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image. Specifically, we allow the generation given an inspirational image of the user's choosing by performing several optimization steps to recover optimal parameters from the model's latent space. We tested several exploration methods from classical gradient descents to gradient-free optimizers. Many gradient-free optimizers just need comparisons (better/worse than another image), so they can even be used without numerical criterion nor inspirational image, only with human preferences. Thus, by iterating on one's preferences we can make robust facial composite or fashion generation algorithms. Our results on four datasets of faces, fashion images, and textures show that satisfactory images are effectively retrieved in most cases.

The impact of external facial features on the construction of facial composites

Witnesses may construct a composite face of a perpetrator using a computerised interface. Police practitioners guide witnesses through this unusual process, the goal being to produce an identifiable image. However, any changes a perpetrator makes to their external facial-features may interfere with this process. In Experiment 1, participants constructed a composite using a holistic interface one day after target encoding. Target faces were unaltered, or had altered external-features: (i) changed hair, (ii) external-features removed or (iii) naturally-concealed external-features (hair, ears, face-shape occluded by a hooded top). These manipulations produced composites with more error-prone internal-features: participants' familiar with a target's unaltered appearance less often provided a correct name. Experiment 2 applied external-feature alterations to composites of unaltered targets; although whole-face composites contained less error-prone internal-features, identification was impaired. Experiment 3 replicated negative effects of changing target hair on construction and tested a practical solution: selectively concealing hair and eyes improved identification. Practitioner Summary: The research indicates that when a target identity disguises or changes hair, this can lead to a witness (or victim) constructing a composite that is less readily identified. We assess a practical method to overcome this forensic issue. Abbreviation: GEE: Generalized Estimating Equations.

Cross-age effects on forensic face construction

The own-age bias (OAB) refers to recognition memory being more accurate for people of our own age than other age groups (e.g., Wright and Stroud, 2002). This paper investigated whether the OAB effect is present during construction of human faces (also known as facial composites, often for forensic/police use). In doing so, it adds to our understanding of factors influencing both facial memory across the life span as well as performance of facial composites. Participant-witnesses were grouped into younger (19–35 years) and older (51–80 years) adults, and constructed a single composite from memory of an own- or cross-age target face using the feature-based composite system PRO-fit. They also completed the shortened version of the glasgow face matching test (GFMT; Burton et al., 2010). A separate group of participants who were familiar with the relevant identities attempted to name the resulting composites. Correct naming of the composites revealed the presence of an OAB for older adults, who constructed more-identifiable composites of own-age than cross-age faces. For younger adults, age of target face did not influence correct naming and their composites were named at the same level as those constructed by older adults for younger targets. Also, there was no reliable correlation between face perception ability and composite quality. Overall, correct naming was fairly good across the experiment, and indicated benefit for older witnesses for older targets. Results are discussed in terms of contemporary theories of OAB, and implications of the work for forensic practice.