Recognition Of Unfamiliar Faces Research Articles

Involuntary Eye-Movement Signatures Differ for Recognition of Oneself, Familiar and Unfamiliar Faces

Involuntary Eye-Movement Signatures Differ for Recognition of Oneself, Familiar and Unfamiliar Faces

A Dynamic Multi-Scale Convolution Model for Face Recognition Using Event-Related Potentials.

With the development of data mining technology, the analysis of event-related potential (ERP) data has evolved from statistical analysis of time-domain features to data-driven techniques based on supervised and unsupervised learning. However, there are still many challenges in understanding the relationship between ERP components and the representation of familiar and unfamiliar faces. To address this, this paper proposes a model based on Dynamic Multi-Scale Convolution for group recognition of familiar and unfamiliar faces. This approach uses generated weight masks for cross-subject familiar/unfamiliar face recognition using a multi-scale model. The model employs a variable-length filter generator to dynamically determine the optimal filter length for time-series samples, thereby capturing features at different time scales. Comparative experiments are conducted to evaluate the model's performance against SOTA models. The results demonstrate that our model achieves impressive outcomes, with a balanced accuracy rate of 93.20% and an F1 score of 88.54%, outperforming the methods used for comparison. The ERP data extracted from different time regions in the model can also provide data-driven technical support for research based on the representation of different ERP components.

Eye movement differences when recognising and learning moving and static faces.

Seeing a face in motion can help subsequent face recognition. Several explanations have been proposed for this "motion advantage," but other factors that might play a role have received less attention. For example, facial movement might enhance recognition by attracting attention to the internal facial features, thereby facilitating identification. However, there is no direct evidence that motion increases attention to regions of the face that facilitate identification (i.e., internal features) compared with static faces. We tested this hypothesis by recording participants' eye movements while they completed the famous face recognition (Experiment 1, N = 32), and face-learning (Experiment 2, N = 60, Experiment 3, N = 68) tasks, with presentation style manipulated (moving or static). Across all three experiments, a motion advantage was found, and participants directed a higher proportion of fixations to the internal features (i.e., eyes, nose, and mouth) of moving faces versus static. Conversely, the proportion of fixations to the internal non-feature area (i.e., cheeks, forehead, chin) and external area (Experiment 3) was significantly reduced for moving compared with static faces (all ps < .05). Results suggest that during both familiar and unfamiliar face recognition, facial motion is associated with increased attention to internal facial features, but only during familiar face recognition is the magnitude of the motion advantage significantly related functionally to the proportion of fixations directed to the internal features.

The role of preSMA and STS in face recognition: A transcranial magnetic stimulation (TMS) study

Current models propose that facial recognition is mediated by two independent yet interacting anatomo-functional systems: one processing facial features mainly mediated by the Fusiform Face Area and the other involved in the extraction of dynamic information from faces, subserved by Superior Temporal Sulcus (STS). Also, the pre-Supplementary Motor Area (pre-SMA) is implicated in facial expression processing as it is involved in its motor mimicry. However, the literature only shows evidence of the implication of STS and preSMA for facial expression recognition, without relating it to face recognition. In addition, the literature shows a facilitatory role of facial motion in the recognition of unfamiliar faces, particularly for poor recognizers. The present study aimed at studying the role of STS and preSMA in unfamiliar face recognition in people with different face recognition skills. 34 healthy participants received repetitive transcranial magnetic stimulation over the right posterior STS, pre-SMA and as sham during a task of matching of faces encoded through: facial expression, rigid head movement or as static (i.e., absence of any facial or head motion). All faces were represented without emotional content. Results indicate that STS has a direct role in recognizing identities through rigid head movement and an indirect role in facial expression processing. This dissociation represents a step forward with respect to current face processing models suggesting that different types of motion involve separate brain and cognitive processes. PreSMA interacts with face recognition skills, increasing the performance of poor recognizers and decreasing that of good recognizers in all presentation conditions. Together, the results suggest the use of at least partially different mechanisms for face recognition in poor and good recognizers and a different role of STS and preSMA in face recognition.

EEG-FRM: a neural network based familiar and unfamiliar face EEG recognition method.

Recognizing familiar faces holds great value in various fields such as medicine, criminal investigation, and lie detection. In this paper, we designed a Complex Trial Protocol-based familiar and unfamiliar face recognition experiment that using self-face information, and collected EEG data from 147 subjects. A novel neural network-based method, the EEG-based Face Recognition Model (EEG-FRM), is proposed in this paper for cross-subject familiar/unfamiliar face recognition, which combines a multi-scale convolutional classification network with the maximum probability mechanism to realize individual face recognition. The multi-scale convolutional neural network extracts temporal information and spatial features from the EEG data, the attention module and supervised contrastive learning module are employed to promote the classification performance. Experimental results on the dataset reveal that familiar face stimuli could evoke significant P300 responses, mainly concentrated in the parietal lobe and nearby regions. Our proposed model achieved impressive results, with a balanced accuracy of 85.64%, a true positive rate of 73.23%, and a false positive rate of 1.96% on the collected dataset, outperforming other compared methods. The experimental results demonstrate the effectiveness and superiority of our proposed model.

Not the norm: Face likeness is not the same as similarity to familiar face prototypes.

Face images depicting the same individual can differ substantially from one another. Ecological variation in pose, expression, lighting, and other sources of appearance variability complicates the recognition and matching of unfamiliar faces, but acquired familiarity leads to the ability to cope with these challenges. Among the many ways that face of the same individual can vary, some images are judged to be better likenesses of familiar individuals than others. Simply put, these images look more like the individual under consideration than others. But what does it mean for an image to be a better likeness than another? Does likeness entail typicality, or is it something distinct from this? We examined the relationship between the likeness of face images and the similarity of those images to average images of target individuals using a set of famous faces selected for reciprocal familiarity/unfamiliarity across US and UK participants. We found that though likeness judgments are correlated with similarity-to-prototype judgments made by both familiar and unfamiliar participants, this correlation was smaller than the correlation between similarity judgments made by different participant groups. This implies that while familiarity weakens the relationship between likeness and similarity-to-prototype judgments, it does not change similarity-to-prototype judgments to the same degree.

A correlational study investigating whether semantic knowledge facilitates face identity processing

ABSTRACT The ability to recognize faces is a fundamental skill in human social interaction. While much research has focused on the recognition of familiar faces, there is growing interest in understanding the cognitive processes underlying the recognition of unfamiliar faces. Previous studies have suggested that both semantic knowledge and physical features play a role in unfamiliar face recognition, but the nature of their relationship is not well understood. This study examines the relationship between unfamiliar face recognition ability and the encoding abilities of semantic knowledge and physical features for famous faces. Using the Gorilla platform, a large group of participants (N = 66) with a broad age range completed three tasks: a challenging unfamiliar face matching task and Famous People Recognition Tests 1 and 2 to evaluate semantic and physical feature encoding abilities, respectively. Results indicate positive correlations between encoding abilities for both semantic knowledge and physical features of familiar faces with Model Face Matching Task scores. Additionally, the encoding ability for semantic knowledge was found to be positively associated with that of physical features.

The importance of internal and external features in recognizing faces that vary in familiarity and race.

Familiar and unfamiliar faces are recognized in fundamentally different ways. One way in which recognition differs is in terms of the features that facilitate recognition: previous studies have shown that familiar face recognition depends more on internal facial features (i.e., eyes, nose and mouth), whereas unfamiliar face recognition depends more on external facial features (i.e., hair, ears and contour). However, very few studies have examined the recognition of faces that vary in both familiarity and race, and the reliance on different facial features, whilst also using faces that incorporate natural within-person variability. In the current study, we used an online version of the card sorting task to assess adults’ (n = 258) recognition of faces that varied in familiarity and race when presented with either the whole face, internal features only, or external features only. Adults better recognized familiar faces than unfamiliar faces in both the whole face and the internal features only conditions, but not in the external features only condition. Reasons why adults did not show an own-race advantage in recognition are discussed.

A more featural based processing for the self-face: An eye-tracking study

Studies have suggested that the holistic advantage in face perception is not always reported for the own face. With two eye-tracking experiments, we explored the role of holistic and featural processing in the processing and the recognition of self, personally familiar, and unfamiliar faces. Observers were asked to freely explore (Exp.1) and recognize (Exp.2) their own, a friend’s, and an unfamiliar face. In Exp.1, self-face was fixated more and longer and there was a preference for the mouth region when seeing the own face and for the nose region when seeing a friend and unfamiliar faces. In Exp.2, the viewing strategies did not differ across all faces, with eye fixations mostly directed to the nose region. These results suggest that task demands might modulate the way that the own face is perceived and highlights the importance of considering the role of the distinct visual experience people have for the own face in the processing and recognition of the self-face.

Introducing the female Cambridge face memory test - long form (F-CFMT+).

The Cambridge Face Memory Test (CFMT) is one of the most used assessments of face recognition abilities in the science of face processing. The original task, using White male faces, has been empirically evaluated for psychometric properties (Duchaine & Nakayama, 2006), while the longer and more difficult version (CFMT+; Russell et al., 2009) has not. Critically, no version exists using female faces. Here, we present the Female Cambridge Face Memory Test – Long Form (F-CFMT+) and evaluate the psychometric properties of this task in comparison to the Male Cambridge Face Memory Test – Long Form (M-CFMT+). We tested typically developing emerging adults (18 to 25 years old) in both Cambridge face recognition tasks, an old-new face recognition task, and a car recognition task. Results indicate that the F-CFMT+ is a valid, internally consistent measure of unfamiliar face recognition that can be used alone or in tandem with the M-CFMT+ to assess recognition abilities for young adult White faces. When used together, performance on the F-CFMT+ and M-CFMT+ can be directly compared, adding to the ability to understand face recognition abilities for different kinds of faces. The two tasks have high convergent validity and relatively good divergent validity with car recognition in the same task paradigm. The F-CFMT+ will be useful to researchers interested in evaluating a broad range of questions about face recognition abilities in both typically developing individuals and those with atypical social information processing abilities.

Is Habitual Sleep Duration an Important Factor for Face Recognition Memory in Young Adults? A Cross-sectional Study

Introduction: Sleep is integral to the process of maintenance and restoration of physical as well as mental health encompassing intelligence, memory as well as emotions. Face recognition memory is a special form of memory that gives the ability to remember people meaningfully and is instrumental in any individual’s meaningful coexistence in the society. Aim: To determine if different habitual sleep durations may be associated with difference in unfamiliar face recognition memory and if there are any gender differences in the same, among young healthy individuals. Materials and Methods: This was an observational analytic cross-sectional study conducted over a month in B.J. Medical College, Ahmedabad, Gujarat, India, including 98 healthy young volunteers (39 females and 59 males) among 1st year students of different professional courses. The participants were subjected to a questionnaire-based interview followed by a face recognition test based on Parkin’s recognition memory test (1995). Mean values and Standard Deviations (SD) were calculated for age, sleep duration, correct and wrong responses in the test and statistical significance was tested by applying t-test with a significance level of p&lt;0.05. Results: The participants were grouped as average-duration (6.6±0.5 hours/day) and long-duration (8.4±1.0 hours/day) sleepers, 53 and 45 in number, respectively, and there was a significant difference in the sleep duration of the two groups (p=8.6×10-17). No significant difference was seen in the number of correct (p=0.08795) and incorrect (p=0.42205) responses of average-duration sleepers and the long-duration sleepers. Gender differences in correct and incorrect responses of average-duration and long-duration sleepers were also not significant. But there was a significant difference in the number of correct responses by females and males, in general. Conclusion: Habitual duration of sleep may not affect face recognition memory in the young ages.

The sibling familiarity effect: Is within?person facial variability shared across siblings?

Humans are experts at familiar face recognition, but poor at unfamiliar face recognition. Familiarity is created when a face is encountered across varied conditions, but the way in which a person's appearance varies is identity?specific, so familiarity with one identity does not benefit recognition of other individuals. However, the faces of biological siblings share structural similarities, so we explored whether the benefits of familiarity are shared across siblings. Results show that familiarity with one half of a sibling pair improves kin detection (experiment 1), and that unfamiliar face matching is more accurate when targets are the siblings of familiar versus unfamiliar individuals (experiment 2). PCA applied to facial images of celebrities and their siblings demonstrates that faces are generally better reconstructed in the principal components of a same?sex sibling than those of an unrelated individual. When we encounter the unfamiliar sibling of someone we already know, our pre?existing representation of their familiar relation may usefully inform processing of the unfamiliar face. This can benefit both kin detection and identity processing, but the benefits are constrained by the degree to which facial variability is shared.

Exploration of face-perceptual ability by EEG induced deep learning algorithm

Face perception essentially refers to an individual's ability to understand and interpret a familiar face. This paper attempts to quantify the face perceptual ability of human subjects using their memory responses, acquired by an electroencephalographic (EEG) device, during engagement of the human subjects in a face recognition task. The entire experimental protocol is designed in the settings of pattern recognition, comprising five main steps, namely artifact removal, feature extraction, classifier training and testing, and face-perception ability measurement. The primary objective of the paper is to design a deep neural network model that utilizes both the temporal and spatial EEG features to categorize the familiar face (FF) and unfamiliar face (UF) responses of human subjects from their brain signals. To extract the spatial information, the acquired raw EEG data is transformed into multi-spectral 2-dimensional images by a deep learning approach. The spatial information, along with the EEG time-frequency domain (temporal) features are then utilized to train the proposed deep neural network, which can preserve the spatial as well as temporal EEG features that are more robust and less sensitive to the variations along each dimension. The proposed deep neural network paradigm demonstrates promising results in classifying the EEG responses for FF and UF recognition with high classification accuracy.Biological underpinning of the cortical mechanism using sLORETA and event related potential (ERP) analysis in perceiving FFs and UFs by both the healthy controls and prosopagnosic patients is also a significant inclusion in the paper. The first and foremost advantage of the proposed work is that it provides a technological means to assess the variations in face perceptual ability of individuals. It can also be used as a biomarker for early prosopagnosia diagnosis.

Contrast Negation Impairs Sorting Unfamiliar Faces by Identity: A Comparison With Original (Contrast-Positive) and Stretched Images.

Recognition of unfamiliar faces is difficult in part due to variations in expressions, angles, and image quality. Studies suggest shape and surface properties play varied roles in face learning, and identification of unfamiliar faces uses diagnostic pigmentation/surface reflectance relative to shape information. Here, participants sorted photo-cards of unfamiliar faces by identity, which were shown in their original, stretched, and contrast-negated forms, to examine the utility of diagnostic shape and surface properties in sorting unfamiliar faces by identity. In four experiments, we varied the presentation order of conditions (contrast-negated first or original first with stretched second across experiments) and whether the same or different photo-cards were seen across conditions. Stretching the images did not impair performance in any measures relative to other conditions. Contrast negation generally exacerbated poor sorting by identity compared with the other conditions. However, seeing the contrast-negated photo-cards last mitigated some of the effects of contrast negation. Together, results suggest an important role for surface properties such as pigmentation and reflectance for sorting by identity and add to literatures on informational content and appearance variability in discrimination of facial identity.

From concepts to percepts in human and machine face recognition: A reply to Blauch, Behrmann & Plaut

Intact recognition of familiar faces is critical for appropriate social interactions. Thus, the human face processing system should be optimized for familiar face recognition. Blauch et al. (2020) used face recognition deep convolutional neural networks (DCNNs) that are trained to maximize recognition of the trained (familiar) identities, to model human unfamiliar and familiar face recognition. In line with this model, we discuss behavioral, neuroimaging and computational findings that indicate that human face recognition develops from the generation of identity-specific concepts of familiar faces that are learned in a supervised manner, to the generation of view-invariant identity-general perceptual representations. Face-trained DCNNs seem to share some fundamental similarities with this framework.

Computational insights into human perceptual expertise for familiar and unfamiliar face recognition

Humans are generally thought to be experts at face recognition, and yet identity perception for unfamiliar faces is surprisingly poor compared to that for familiar faces. Prior theoretical work has argued that unfamiliar face identity perception suffers because the majority of identity-invariant visual variability is idiosyncratic to each identity, and thus, each face identity must be learned essentially from scratch. Using a high-performing deep convolutional neural network, we evaluate this claim by examining the effects of visual experience in untrained, object-expert and face-expert networks. We found that only face training led to substantial generalization in an identity verification task of novel unfamiliar identities. Moreover, generalization increased with the number of previously learned identities, highlighting the generality of identity-invariant information in face images. To better understand how familiarity builds upon generic face representations, we simulated familiarization with face identities by fine-tuning the network on images of the previously unfamiliar identities. Familiarization produced a sharp boost in verification, but only approached ceiling performance in the networks that were highly trained on faces. Moreover, in these face-expert networks, the sharp familiarity benefit was seen only at the identity-based output probability layer, and did not depend on changes to perceptual representations; rather, familiarity effects required learning only at the level of identity readout from a fixed expert representation. Our results thus reconcile the existence of a large familiar face advantage with claims that both familiar and unfamiliar face identity processing depend on shared expert perceptual representations.

Exploring the relationship between mindfulness, compassion and unfamiliar face identification

ABSTRACTResearch has related empathy to face recognition. Empathy is proposed as part of compassion, while compassion relates to self-compassion and mindfulness, which may be more effective in face recognition than empathy alone. Experiment 1 investigated the relationship between compassion, self-compassion and empathy, and unfamiliar face recognition memory and matching. Self-compassion, including its facets of mindfulness and common humanity, were related to better recognition, but not matching. Results did not support a relationship between face identification and empathy or compassion. Experiments 2 and 3 further investigated common humanity and mindfulness and found both constructs to relate to face recognition memory. Furthermore, aspects of mindfulness (i.e., act aware and describe) positively related to face recognition. Experiment 3 used an array matching task to further show that matching did not relate to mindfulness or common humanity. The present findings may inform new directions in face recognition research, aiming to practically assist face recognition.

Description Benefits, Production Benefits, and Context Retrieval for Recognition of Unfamiliar Faces

Abstract Several studies have shown that giving brief descriptions for unfamiliar faces can lead to better episodic recognition memory relative to faces in a view-only (control) condition. More broadly, this finding is consistent with the benefits of elaboration during study on later recognition. Two articles have reported description benefits when participants did not know the study condition (control or description) for a trial until after a face was removed from view (Jones, Armstrong, Casey, Burson, &amp; Memon, 2013; Jones, Robinson, &amp; Steel, 2018). We conducted 3 experiments to shed additional light on the basis for this benefit, and the measures we used were old–new recognition and recognition confidence for the face and the proportion of test faces with recall for descriptive information. In Experiments 1 and 2 participants were explicitly instructed to describe each study face and to type the description or not (control condition). There was a numerical advantage for “old” responses, with the difference being significant in Experiment 1. A significant advantage for the typed condition occurred for “very sure old” and recall responses for both experiments. The advantage occurred across study repetition conditions in Experiment 1 and when participants were given plenty of time to generate a description in Experiment 2. Overall, these findings indicate that physically typing the description leads to a small advantage in recollection relative to a condition without physical production (i.e., a type of physical production effect). However, the production advantage was not obtained on any measure in Experiment 3 when the control condition required participants to type the number of words in the description.

Hemodynamic response to familiar faces in children with ADHD

BackgroundSchool-age children with attention deficit hyperactivity disorder (ADHD) have difficulties in interpersonal relationships, in addition to impaired facial expression perception and recognition. For successful social interactions, the ability to discriminate between familiar and unfamiliar faces is critical. However, there are no published reports on the recognition of familiar and unfamiliar faces by children with ADHD.MethodsWe evaluated the neural correlates of familiar and unfamiliar facial recognition in children with ADHD compared to typically developing (TD) children. We used functional near-infrared spectroscopy (fNIRS) to measure hemodynamic responses on the bilateral temporal regions while participants looked at photographs of familiar and unfamiliar faces. Nine boys with ADHD and 14 age-matched TD boys participated in the study. fNIRS data were Z-scored prior to analysis.ResultsDuring familiar face processing, TD children only showed significant activity in the late phase, while ADHD children showed significant activity in both the early and late phases. Additionally, the boys with ADHD did not show right hemispheric lateralization to familiar faces.ConclusionsThis study is the first to assess brain activity during familiar face processing in boys with ADHD using fNIRS. These findings of atypical patterns of brain activity in boys with ADHD may be related to social cognitive impairments from ADHD.

Recognition of Famous and Unfamiliar Faces among Patients Suffering from Amnesia Mild Cognitive Impairment (AMCI) and Alzheimer’s Disease

Objective: Memory assessment for the early diagnosis of cortical dementia is a complicated process which depends on important factors such as facial recognition and naming. These factors could be considered to carry a predictive power to detect neurodegenerative disorders. The present study aimed to study and compare naming or recognizing famous faces with the recognition of newly learned faces among patients with Amnesia Mild Cognitive Impairment (AMCI) and Alzheimer's disease. Method : To collect data, 60 AMCI patients, 62 patients suffering from Alzheimer's disease, and 63 cognitively healthy individuals were assessed using Wechsler Memory Scale-III Faces test (WMS-III faces) and Famous Faces test. Results: The results of one-way ANOVA indicated that the patients suffering from AMCI and Alzheimer's disease scored significantly worse than the control group on naming (p < 0.001), recognition (p < 0.001) section of the Famous Faces test, and immediate or delayed recognition on the WMS-III Faces test (p < 0.001). Also, the obtained results showed that the patients groups received lower scores on WMS-III Faces compared to the Famous Faces test. Conclusion: The results of this study suggested that the unfamiliar and Famous Faces tests allow the quantification of patients' face recognition and name recall abilities which, in turn, makes it possible to make more accurate predictions about cases of dementia. These tests can be used for clinical and research purposes to screen those who may be prone to dementia and need further neuropsychological assessment.