Facial identity across the lifespan

A narrow band of image dimensions is critical for face recognition

A key challenge in human and computer face recognition is to differentiate information that is diagnostic for identity from other sources of image variation. Here, we used a combined computational and behavioural approach to reveal critical image dimensions for face recognition. Behavioural data were collected using a sorting and matching task with unfamiliar faces and a recognition task with familiar faces. Principal components analysis was used to reveal the dimensions across which the shape and texture of faces in these tasks varied. We then asked which image dimensions were able to predict behavioural performance across these tasks. We found that the ability to predict behavioural responses in the unfamiliar face tasks increased when the early PCA dimensions (i.e. those accounting for most variance) of shape and texture were removed from the analysis. Image similarity also predicted the output of a computer model of face recognition, but again only when the early image dimensions were removed from the analysis. Finally, we found that recognition of familiar faces increased when the early image dimensions were removed, decreased when intermediate dimensions were removed, but then returned to baseline recognition when only later dimensions were removed. Together, these findings suggest that early image dimensions reflect ambient changes, such as changes in viewpoint or lighting, that do not contribute to face recognition. However, there is a narrow band of image dimensions for shape and texture that are critical for the recognition of identity in humans and computer models of face recognition.

Understanding face matching

Many security settings rely on the identity matching of unfamiliar people, which has led this task to be studied extensively in Cognitive Psychology. In these experiments, observers typically decide whether pairs of faces depict one person (an identity match) or two different people (an identity mismatch). The visual similarity of the to-be-compared faces must play a primary role in how observers accurately resolve this task, but the nature of this similarity-accuracy relationship is unclear. The current study investigated the association between accuracy and facial similarity at the level of individual items (Experiments 1 and 2) and facial features (Experiments 3 and 4). All experiments demonstrate a strong link between similarity and matching accuracy, indicating that this forms the basis of identification decisions. At a feature level, however, similarity exhibited distinct relationships with match and mismatch accuracy. In matches, similarity information was generally shared across the features of a face pair under comparison, with greater similarity linked to higher accuracy. Conversely, features within mismatching face pairs exhibited greater variation in similarity information. This indicates that identity matches and mismatches are characterised by different similarity profiles, which present distinct challenges to the cognitive system. We propose that these identification decisions can be resolved through the accumulation of convergent featural information in matches and the evaluation of divergent featural information in mismatches.

Children's ability to recognize their parent's face improves with age

Adults are experts at recognizing familiar faces across images that incorporate natural within-person variability in appearance (i.e., ambient images). Little is known about children's ability to do so. In the current study, we investigated whether 4- to 7-year-olds (n = 56) could recognize images of their own parent-a person with whom children have had abundant exposure in a variety of different contexts. Children were asked to identify images of their parent that were intermixed with images of other people. We included images of each parent taken both before and after their child was born to manipulate how close the images were to the child's own experience. When viewing before-birth images, 4- and 5-year-olds were less sensitive to identity than were older children; sensitivity did not differ when viewing images taken after the child was born. These findings suggest that with even the most familiar face, 4- and 5-year-olds have difficulty recognizing instances that go beyond their direct experience. We discuss two factors that may contribute to the prolonged development of familiar face recognition.

What happens to our representation of identity as familiar faces age? Evidence from priming and identity aftereffects

Matching identity in images of unfamiliar faces is error prone, but we can easily recognize highly variable images of familiar faces – even images taken decades apart. Recent theoretical development based on computational modelling can account for how we recognize extremely variable instances of the same identity. We provide complementary behavioural data by examining older adults’ representation of older celebrities who were also famous when young. In Experiment 1, participants completed a long‐lag repetition priming task in which primes and test stimuli were the same age or different ages. In Experiment 2, participants completed an identity after effects task in which the adapting stimulus was an older or young photograph of one celebrity and the test stimulus was a morph between the adapting identity and a different celebrity; the adapting stimulus was the same age as the test stimulus on some trials (e.g., both old) or a different age (e.g., adapter young, test stimulus old). The magnitude of priming and identity after effects were not influenced by whether the prime and adapting stimulus were the same age or different age as the test face. Collectively, our findings suggest that humans have one common mental representation for a familiar face (e.g., Paul McCartney) that incorporates visual changes across decades, rather than multiple age‐specific representations. These findings make novel predictions for state‐of‐the‐art algorithms (e.g., Deep Convolutional Neural Networks).

Naturalistic Face Learning in Infants and Adults

Everyday face recognition presents a difficult challenge because faces vary naturally in appearance as a result of changes in lighting, expression, viewing angle, and hairstyle. We know little about how humans develop the ability to learn faces despite natural facial variability. In the current study, we provide the first examination of attentional mechanisms underlying adults' and infants' learning of naturally varying faces. Adults (n = 48) and 6- to 12-month-old infants (n = 48) viewed videos of models reading a storybook; the facial appearance of these models was either high or low in variability. Participants then viewed the learned face paired with a novel face. Infants showed adultlike prioritization of face over nonface regions; both age groups fixated the face region more in the high- than low-variability condition. Overall, however, infants showed less ability to resist contextual distractions during learning, which potentially contributed to their lack of discrimination between the learned and novel faces. Mechanisms underlying face learning across natural variability are discussed.

The Episodic Prototypes Model (EPM): On the nature and genesis of facial representations

Faces undergo massive changes over time and life events. We need a mental representation which is flexible enough to cope with the existing visual varieties, but which is also stable enough to be the basis for valid recognition. Two main theoretical frameworks exist to describe facial representations: prototype models assuming one central item comprising all visual experiences of a face, and exemplar models assuming single representations of each visual experience of a face. We introduce a much more ecological valid model dealing with episodic prototypes (the Episodic Prototypes Model—EPM), where faces are represented by a low number of prototypes that refer to specific Episodes of Life (EoL, e.g., early adulthood, mature age) during which the facial appearance shows only moderate variation. Such an episodic view of mental representation allows for efficient storage, as the number of needed prototypes is relatively low, and it allows for the needed variation within a prototype that keeps the everyday and steadily ongoing changes across a certain period of time. Studies 1–3 provide evidence that facial representations are highly dependent on temporal aspects which is in accord with EoL, and that individual learning history generates the structure and content of respective prototypes. In Study 4, we used implicit measures (RT) in a face verification task to investigate the postulated power of the EPM. We could demonstrate that episodic prototypes clearly outperformed visual depictions of exhaustive prototypes, supporting the general idea of our approach.

Partitioning natural face image variability emphasises within-identity over between-identity representation for understanding accurate recognition

Accurately recognising faces enables social interactions. In recent years it has become clear that people's accuracy differs markedly depending on viewer's familiarity with a face and their individual skill, but the cognitive and neural bases of these accuracy differences are not understood. We examined cognitive representations underlying these accuracy differences by measuring similarity ratings to natural facial image variation. Natural variation was sampled from uncontrolled images on the internet to reflect the appearance of faces as they are encountered in daily life. Using image averaging, and inspired by the computation of Analysis of Variance, we partitioned this variation into differences between faces (between-identity variation) and differences between photos of the same face (within-identity variation). This allowed us to compare modulation of these two sources of variation attributable to: (i) a person's familiarity with a face and, (ii) their face recognition ability. Contrary to prevailing accounts of human face recognition and perceptual learning, we found that modulation of within-identity variation - rather than between-identity variation - was associated with high accuracy. First, familiarity modulated similarity ratings to within-identity variation more than to between-face variation. Second, viewers that are extremely accurate in face recognition - 'super-recognisers' - differed from typical perceivers mostly in their ratings of within-identity variation, compared to between-identity variation. In a final computational analysis, we found evidence that transformations of between- and within-identity variation make separable contributions to perceptual expertise in face recognition. We conclude that inter- and intra-individual accuracy differences primarily arise from differences in the representation of within-identity image variation.

Avatars with faces of real people: A construction method for scientific experiments in virtual reality

Experimental psychology research typically employs methods that greatly simplify the real-world conditions within which cognition occurs. This approach has been successful for isolating cognitive processes, but cannot adequately capture how perception operates in complex environments. In turn, real-world environments rarely afford the access and control required for rigorous scientific experimentation. In recent years, technology has advanced to provide a solution to these problems, through the development of affordable high-capability virtual reality (VR) equipment. The application of VR is now increasing rapidly in psychology, but the realism of its avatars, and the extent to which they visually represent real people, is captured poorly in current VR experiments. Here, we demonstrate a user-friendly method for creating photo-realistic avatars of real people and provide a series of studies to demonstrate their psychological characteristics. We show that avatar faces of familiar people are recognised with high accuracy (Study 1), replicate the familiarity advantage typically observed in real-world face matching (Study 2), and show that these avatars produce a similarity-space that corresponds closely with real photographs of the same faces (Study 3). These studies open the way to conducting psychological experiments on visual perception and social cognition with increased realism in VR.

Insights from computational models of face recognition: A reply to Blauch, Behrmann and Plaut

We agree with Blauch, Behrmann, and Plaut (2020) on a number of points, and are reassured that their data bear out our previous findings. We discuss differences in modelling style, and the usefulness of different types of model for supporting psychological understanding. We emphasise the role that within-person variability plays in recognising familiar faces and clarify the range over which it is idiosyncratic. The combination of image analysis with top-down support to cohere different images of the same person seems to be an important characteristic of successful models.