Facial expressions are inherently dynamic, and our visual system is sensitive to subtle changes in their temporal sequence. However, researchers often use dynamic morphs of photographs—simplified, linear representations of motion—to study the neural correlates of dynamic face perception. To explore the brain's sensitivity to natural facial motion, we constructed a novel dynamic face database using generative neural networks, trained on a verified set of video-recorded emotional expressions. The resulting deepfakes, consciously indistinguishable from videos, enabled us to separate biological motion from photorealistic form. Results showed that conventional dynamic morphs elicit distinct responses in the brain compared to videos and photos, suggesting they violate expectations (n400) and have reduced social salience (late positive potential). This suggests that dynamic morphs misrepresent facial dynamism, resulting in misleading insights about the neural and behavioural correlates of face perception. Deepfakes and videos elicited largely similar neural responses, suggesting they could be used as a proxy for real faces in vision research, where video recordings cannot be experimentally manipulated. And yet, despite being consciously undetectable as fake, deepfakes elicited an expectation violation response in the brain. This points to a neural sensitivity to naturalistic facial motion, beyond conscious awareness. Despite some differences in neural responses, the realism and manipulability of deepfakes make them a valuable asset for research where videos are unfeasible. Using these stimuli, we proposed a novel marker for the conscious perception of naturalistic facial motion – Frontal delta activity – which was elevated for videos and deepfakes, but not for photos or dynamic morphs.

In 2014, the US National Research Council called for the development of new lineup technologies to increase eyewitness identification accuracy (National Research Council, 2014). In a police lineup, a suspect is presented alongside multiple individuals known to be innocent who resemble the suspect in physical appearance know as fillers. A correct identification decision by an eyewitness can lead to a guilty suspect being convicted or an innocent suspect being exonerated from suspicion. An incorrect decision can result in the perpetrator remaining at large, or even a wrongful conviction of a mistakenly identified person. Incorrect decisions carry considerable human and financial costs, so it is essential to develop and enact lineup procedures that maximise discrimination accuracy, or the witness’ ability to distinguish guilty from innocent suspects. This talk focuses on new technology and innovation in the field of eyewitness identification. We will focus on the interactive lineup, which is a procedure that we developed based on research and theory from the basic science literature on face perception and recognition. The interactive lineup enables witnesses to actively explore and dynamically view the lineup members. The procedure has been shown to maximize discrimination accuracy, which is the witness’ ability to discriminate guilty from innocent suspects. The talk will conclude by reflecting on emerging technological frontiers and research opportunities.

Despite her still poor visual acuity and minimal visual experience, a 2-3 month old baby will reliably respond to facial expressions, smiling back at her caretaker or older sibling. But what if that same baby had been deprived of her early visual experience? Will she be able to appropriately respond to seemingly mundane interactions, such as a peer’s facial expression, if she begins seeing at the age of 10? My work is part of Project Prakash, a dual humanitarian/scientific mission to identify and treat curably blind children in India and then study how their brain learns to make sense of the visual world when their visual journey begins late in life. In my talk, I will give a brief overview of Project Prakash, and present findings from one of my primary lines of research: plasticity of face perception with late sight onset. Specifically, I will discuss a mixed methods effort to probe and explain the differential windows of plasticity that we find across different aspects of distributed face recognition, from distinguishing a face from a nonface early in the developmental trajectory, to recognizing facial expressions, identifying individuals, and even identifying one’s own caretaker. I will draw connections between our empirical findings and our recent theoretical work hypothesizing that children with late sight onset may suffer persistent face identification difficulties because of the unusual acuity progression they experience relative to typically developing infants. Finally, time permitting, I will point to potential implications of our findings in supporting newly-sighted children as they transition back into society and school, given that their needs and possibilities significantly change upon the introduction of vision into their lives.

I describe a well-tested Python module that does automated alignment and warping of faces images, and some advantages over existing solutions. An additional tool I’ve developed does automated extraction of facial features, which can be used in a number of interesting ways. I illustrate the value of wavelet-based features with a brief description of 2 recent studies: perceptual in-painting, and the robustness of the whole-part advantage across a large stimulus set. Finally, I discuss the suitability of various deep learning models for generating stimuli to study perceptual face spaces. I believe those interested in the forensic aspects of face perception may find this talk useful.

3D Morphable Models are my favorite class of generative models and are commonly used to model faces. They are typically applied to ill-posed problems such as 3D reconstruction from 2D data. I'll start my presentation with an introduction into 3D Morphable Models and show what they are capable of doing. I'll then focus on our recent finding, the Identity-Expression Ambiguity: We demonstrate that non-orthogonality of the variation in identity and expression can cause identity-expression ambiguity in 3D Morphable Models, and that in practice expression and identity are far from orthogonal and can explain each other surprisingly well. Whilst previously reported ambiguities only arise in an inverse rendering setting, identity-expression ambiguity emerges in the 3D shape generation process itself. The goal of this presentation is to demonstrate the ambiguity and discuss its potential consequences in a computer vision setting as well as for understanding face perception mechanisms in the human brain.

Despite the common advice “not to judge a book by its cover”, we form impressions of character within a second of seeing a stranger’s face. These impressions have widespread consequences for society and for the economy, making it vital that we have a clear theoretical understanding of which impressions are important and how they are formed. In my talk, I outline a data-driven approach to answering these questions, starting by building models of the key dimensions underlying impressions of naturalistic face images. Overall, my findings suggest deeper links between the fields of face perception and social stereotyping than have previously been recognised.

Prosopometamorphopsia (PMO) is a disorder characterized by face perception distortions. People with PMO see facial features that appear to melt, stretch, and change size and position. I'll discuss research on PMO carried out by my lab and others that sheds light on the cognitive and neural organization of face perception. https://facedistortion.faceblind.org/

The successful recognition of familiar persons is critical for social interactions. Despite extensive research on the neural representations of familiar faces, we know little about how such representations unfold as someone becomes familiar. In three EEG experiments, we elucidated how representations of face familiarity and identity emerge from different qualities of familiarization: brief perceptual exposure (Experiment 1), extensive media familiarization (Experiment 2) and real-life personal familiarization (Experiment 3). Time-resolved representational similarity analysis revealed that familiarization quality has a profound impact on representations of face familiarity: they were strongly visible after personal familiarization, weaker after media familiarization, and absent after perceptual familiarization. Across all experiments, we found no enhancement of face identity representation, suggesting that familiarity and identity representations emerge independently during face familiarization. Our results emphasize the importance of extensive, real-life familiarization for the emergence of robust face familiarity representations, constraining models of face perception and recognition memory.

Face matching tests have traditionally been developed to assess human face perception in the neurotypical range, but methods that underlie their development often make it difficult for these measures to be applied in atypical populations (developmental prosopagnosics, super recognizers) due to unadjusted difficulty. We have recently presented the development of the Oxford Face Matching Test, a measure that bases individual item-difficulty on algorithmically derived similarity of presented stimuli. The measure seems useful as it can be given online or in-laboratory, has good discriminability and high test-retest reliability in the neurotypical groups. In addition, it has good validity in separating atypical groups at either of the spectrum ends. In this talk, I examine the stability of the OFMT and other traditionally used measures in atypical groups. On top of the theoretical significance of determining whether reliability of tests is equivalent in atypical population, this is an important question because of the practical concerns of retesting the same participants across different lab groups. Theoretical and practical implications for further test development and data sharing are discussed.