Facial Identity Recognition Research Articles

The neural basis of human unfamiliar face identity recognition with fMRI frequency-tagging

The neural basis of human unfamiliar face identity recognition with fMRI frequency-tagging

Intracerebral recordings evidence that unfamiliar face-identity recognition is supported by face-selective neural populations in the human ventral occipito-temporal cortex

Intracerebral recordings evidence that unfamiliar face-identity recognition is supported by face-selective neural populations in the human ventral occipito-temporal cortex

Exploration of the Links Between Psychosocial Well-being and Face Recognition Skills in a French-Speaking Sample.

Face recognition abilities vary tremendously in the general population. People at the lower end of the spectrum, those with developmental prosopagnosia, report stress, anxiety or social interaction issues due to their poor face recognition abilities. It is thus important to develop adequate diagnostic tools convenient to use for clinicians and to examine relationships between face recognition skills and negative affects. In the present study, we provide a validated French translation of the 20-item prosopagnosia index (PI20), a self-report measure used to detect people with developmental facial identity recognition deficits (Shah et al., 2015; Tsantani et al., 2021). We also examined links between face recognition skills measured with the PI20 and a standard face recognition test (Cambridge face memory test-CFMT; Duchaine & Nakayama, 2006) and measures of social anxiety (social interaction anxiety scale, social phobia scale) and negative affects (state trait anxiety scale, Beck depression inventory). We did not find any significant correlation between the CFMT and measures of psychosocial well-being and only found a weak positive association between the PI20 and social interaction anxiety. Although this association is weak and warrants further research, raising awareness about developmental face recognition issues may help improve the well-being of people with facial identity recognition deficits and provide new investigation or intervention avenues for clinicians who treat patients with social interaction anxiety.

The temporal dynamics of familiar face recognition: Event-related brain potentials reveal the efficient activation of facial identity representations

While it is widely known that humans are typically highly accurate at recognizing familiar faces, it is less clear how efficiently recognition is achieved. In a series of three experiments, we used event-related brain potentials (ERP) in a repetition priming paradigm to examine the efficiency of familiar face recognition. Specifically, we varied the presentation time of the prime stimulus between 500 ms and 33 ms (Experiments 1 and 2), and additionally used backward masks (Experiment 3) to prevent the potential occurrence of visual aftereffects. Crucially, to test for the recognition of facial identity rather than a specific picture, we used different images of the same facial identities in repetition conditions. We observed clear ERP repetition priming effects between 300 and 500 ms after target onset at all prime durations, which suggests that the prime stimulus was sufficiently well processed to allow for facilitated recognition of the target in all conditions. This finding held true even in severely restricted viewing conditions including very brief prime durations and backward masks. We conclude that the facial recognition system is both highly effective and efficient, thus allowing for our impressive ability to recognise the faces that we know.

Identity recognition from faces and bodies in schizophrenia spectrum disorders

Deficits in facial identity recognition and its association with poor social functioning are well documented in schizophrenia, but none of these studies have assessed the role of the body in these processes. Recent research in healthy populations shows that the body is also an important source of information in identity recognition, and the current study aimed to thoroughly examine identity recognition from both faces and bodies in schizophrenia. Sixty-five individuals with schizophrenia and forty-nine healthy controls completed three conditions of an identity matching task in which they attempted to match unidentified persons in unedited photos of faces and bodies, edited photos showing faces only, or edited photos showing bodies only. Results revealed global deficits in identity recognition in individuals with schizophrenia (ηp2 = 0.068), but both groups showed better recognition from bodies alone as compared to faces alone (ηp2 = 0.573), suggesting that the ability to extract useful information from bodies when identifying persons may remain partially preserved in schizophrenia. Further research is necessary to understand the relationship between face/body processing, identity recognition, and functional outcomes in individuals with schizophrenia-spectrum disorders.

Autistic Individuals Do Not Alter Visual Processing Strategy During Encoding Versus Recognition of Faces: A Hidden Markov Modeling Approach.

Visual face recognition-the ability to encode, discriminate, and recognize the faces of others-is fundamentally supported by eye movements and is a common source of difficulty for autistic individuals. We aimed to evaluate how visual processing strategies (i.e., eye movement patterns) directly support encoding and recognition of faces in autistic and neurotypical (NT) individuals. We used a hidden Markov modeling approach to evaluate the spatiotemporal dynamics of eye movements in autistic (n = 15) and neurotypical (NT) adolescents (n = 17) during a face identity recognition task. We discovered distinct eye movement patterns among all participants, which included a focused and exploratory strategy. When evaluating change in visual processing strategy across encoding and recognition phases, autistic individuals did not shift their eye movement patterns like their NT peers, who shifted to a more exploratory visual processing strategy during recognition. These findings suggest that autistic individuals do not modulate their visual processing strategy across encoding and recognition of faces, which may be an indicator of less efficient face processing.

SqueezeNet: An Improved Lightweight Neural Network for Sheep Facial Recognition

To quickly realize facial identity recognition in sheep, this paper proposes a lightweight detection algorithm based on SSD with a self-constructed dataset. Firstly, the algorithm replaces the VGG16 backbone of SSD with the lightweight neural network SqueezeNet, creating a lightweight hybrid network model. Secondly, an ECA mechanism is introduced at the front end of the pooling layer with a parameter volume of 12 × 1000 into the feature extraction network. Lastly, the smoothL1 loss function is replaced with the BalancedL1 loss function. The optimal model size has been reduced from the original SSD’s 132 MB to 35.8 MB. The average precision is 82.39%, and the mean frame rate is 66.11 frames per second. Compared to the baseline SSD model, the average precision has improved by 2.17%, the model volume has decreased by 96.2 MB, and the detection speed has increased by 7.13 frames per second. Using the same dataset on different target detection models for comparison tests, the average accuracy mean values are improved by 2.17%, 3.63%, and 1.30% compared to the SSD model, Faster R-CNN model, and Retinanet model, respectively, which substantiates a better overall performance compared to the pre-improvement model. This paper proposes an improved model that significantly reduces the model size and its computation while keeping the model performance at a high level, providing a methodological reference for the digitization of livestock farming.

Familial Transmission of Developmental Prosopagnosia: New Case Reports from an Extended Family and Identical Twins.

Existing evidence suggests that developmental prosopagnosia (DP) is a surprisingly prevalent condition, with some individuals describing lifelong difficulties with facial identity recognition. Together with case reports of multiple family members with the condition, this evidence suggests that DP is inherited in at least some instances. Here, we offer some novel case series that further support the heritability of the condition. First, we describe five adult siblings who presented to our lab with symptoms of DP. Second, for the first known time in the literature, we describe a pair of adult identical twins who contacted us in the belief that they both experience DP. The condition was confirmed in three of the five siblings (with minor symptoms observed in the remaining two) and in both twins. Supplementary assessments suggested that all individuals also experienced some degree of difficulty with facial identity perception, but that object recognition was preserved. These findings bolster the evidence supporting the heritability of DP and suggest that it can be a specific impairment in some cases.

Intracerebral electrical stimulation of the face-selective right lateral fusiform gyrus transiently impairs face identity recognition

Neuroimaging and intracranial electrophysiological studies have consistently shown the largest and most consistent face-selective neural activity in the middle portion of the human right lateral fusiform gyrus (‘fusiform face area(s)’, FFA). Yet, direct evidence for the critical role of this region in face identity recognition (FIR) is still lacking. Here we report the first evidence of transient behavioral impairment of FIR during focal electrical stimulation of the right FFA. Upon stimulation of an electrode contact within this region, subject CJ, who shows typical FIR ability outside of stimulation, was transiently unable to point to pictures of famous faces among strangers and to match pictures of famous or unfamiliar faces presented simultaneously for their identity. Her performance at comparable tasks with other visual materials (written names, pictures of buildings) remained unaffected by stimulation at the same location. During right FFA stimulation, CJ consistently reported that simultaneously presented faces appeared as being the same identity, with little or no distortion of the spatial face configuration. Independent electrophysiological recordings showed the largest neural face-selective and face identity activity at the critical electrode contacts. Altogether, this extensive multimodal case report supports the causal role of the right FFA in FIR.

A neural marker of the human face identity familiarity effect

Human adults associate different views of an identity much better for familiar than for unfamiliar faces. However, a robust and consistent neural index of this behavioral face identity familiarity effect (FIFE)—not found in non-human primate species—is lacking. Here we provide such a neural FIFE index, measured implicitly and with one fixation per face. Fourteen participants viewed 70 s stimulation sequences of a large set (n = 40) of widely variable natural images of a face identity at a rate of 6 images/second (6 Hz). Different face identities appeared every 5th image (1.2 Hz). In a sequence, face images were either familiar (i.e., famous) or unfamiliar, participants performing a non-periodic task unrelated to face recognition. The face identity recognition response identified at 1.2 Hz over occipital-temporal regions in the frequency-domain electroencephalogram was 3.4 times larger for familiar than unfamiliar faces. The neural response to familiar faces—which emerged at about 180 ms following face onset—was significant in each individual but a case of prosopdysgnosia. Besides potential clinical and forensic applications to implicitly measure one’s knowledge of a face identity, these findings open new perspectives to clarify the neurofunctional source of the FIFE and understand the nature of human face identity recognition.

Create and implement a new method for robust video face recognition using convolutional neural network algorithm

Biometric applications have massive demand in today's era. The areas of applications are mostly linked with the security of the system. Biometric features are regarded as the primary resource for security purposes due to their own distinctiveness and non-volatile essence. System authentication using biometrics is considered to be a sophisticated technology. Noise effect inducts variation in the biometric subject that causes an adverse impact on establishing the recognition. The proposed model supported the development of an effective method for performing facial biometric feature recognition. The model's goal is to reduce the number of false approvals and refusals. The proposed algorithm has been applied over a video dataset containing surveillance video frames that captures facial subjects dynamically. The first step is pre-processing of the video frames that have been carried out in the proposed model. Then, the Viola-Jones algorithm was applied to detect the facial subjects in the video frames. Feature extraction from the facial subject has been accomplished by applying a deep reinforcement learning algorithm. Further, the proposed model applied a convolutional neural network (CNN) algorithm to perform feature recognition of facial identity accurately. The proposed technique aims to maintain a huge recognition rate of dynamic facial subjects under various unprecedented noise variations. In the classification algorithm, the recognition accuracy is found to be 98.85%.

Discrimination of facial identity based on simple contrast patterns generated by shading and shadows

The pattern of shadows and shading across a face is determined partly by face shape and may therefore provide a cue for facial recognition. In this study, we measured the ability of human observers to discriminate facial identity based simply on the coarse pattern of contrast produced by the interaction between facial geometry and lighting direction. We used highly realistic 3D models of human heads to create images of faces illuminated from different horizontal and vertical directions, which were then converted to two-tone images (‘Mooney faces’) to isolate the coarse pattern of contrast. Participants were presented with pairs of two-tone faces and judged whether it was the same person in both images. Participants could discriminate facial identity based on the minimal cues within the two-tone images, though sensitivity depended on the horizontal and vertical lighting direction. Performance on the Mooney recognition task correlated with general facial recognition ability, though the role of face-specific processing in this relationship was not significant. Our results demonstrate that shading information in the form of simple contrast cues is sufficient for discriminating facial identity, and support the idea that visual processing is somewhat optimised for overhead lighting – here, in the relatively high-level context of face identity recognition.

Why can people with developmental prosopagnosia recognise some familiar faces? Insights from subjective experience.

Developmental prosopagnosia is a relatively common visuo-cognitive condition, characterised by impaired facial identity recognition. Impairment severity appears to reside on a continuum, however, it is unknown whether instances of milder deficits reflect the successful use of spontaneous (typical) face recognition strategies, or the application of extraneous compensatory cues to recognition. Here, we explore this issue in two studies. First, 23 adults with developmental prosopagnosia were asked about their use of spontaneous versus compensatory face recognition techniques in everyday life, using a series of closed- and open-ended questions. Second, the same participants performed a computerised famous face recognition task where they were asked to provide reasons why they could make any successful identifications. Findings from both studies suggest that people with developmental prosopagnosia can successfully, and quite frequently, use compensatory strategies to recognition, and that these cues support the majority of instances of preserved familiar face recognition. In contrast, 16 of the 23 participants were able to spontaneously recognise familiar faces on at least some occasions, but there were vast individual differences in frequencies of success. These findings have important implications for our conceptualisation of the condition, as well as for diagnostic practice.

Altering stimulus timing via fast rhythmic sensory stimulation induces STDP-like recall performance in human episodic memory

Episodic memory provides humans with the ability to mentally travel back to the past,1 where experiences typically involve associations between multimodal information. Forming a memory of the association is thought to be dependent on modification of synaptic connectivity.2,3 Animal studies suggest that the strength of synaptic modification depends on spike timing between pre- and post-synaptic neurons on the order of tens of milliseconds, which is termed "spike-timing-dependent plasticity" (STDP).4 Evidence found in human invitro studies suggests different temporal scales in long-term potentiation (LTP) and depression (LTD), compared with the critical time window of STDP in animals.5,6 In the healthy human brain, STDP-like effects have been shown in the motor cortex, visual perception, and face identity recognition.7,8,9,10,11,12,13 However, evidence in human episodic memory is lacking. We investigated this using rhythmic sensory stimulation to drive visual and auditory cortices at 37.5Hz with four phase offsets. Visual relative to auditory cued recall accuracy was significantly enhanced in the 90° condition when the visual stimulus led at the shortest delay (6.67ms). This pattern was reversed in the 270° condition when the auditory stimulus led at the shortest delay. Within cue modality, recall was enhanced when a stimulus of the corresponding modality led the shortest delay (6.67ms) compared with the longest delay (20ms). Our findings provide evidence for STDP in human episodic memory, which builds an important bridge from invitro studies in animals to human memory behavior.

Stability of face recognition abilities after left or right anterior temporal lobectomy.

Patients with anterior temporal lobe (ATL) resection due to mesial temporal lobe epilepsy (MTLE) have difficulties at identifying familiar faces and explicitly remembering newly learned faces but their ability to individuate unfamiliar faces remains largely unknown. Moreover, the extent to which their difficulties with familiar face identity recognition and learning is truly due to the ATL resection remains unknown. Here, we report a study of 24 MTLE patients and matched healthy controls tested with an extensive set of seven face and visual object recognition tasks (including three tasks evaluating unfamiliar face individuation) before and about 6 months after unilateral (nine left, 15 right) ATL resection. We found that ATL resection has little or no effect on the patients' preserved pre-surgical ability to perform unfamiliar face individuation, both at the group and individual levels. More surprisingly, ATL resection also has little effect on the patients' performance at recognizing and naming famous faces as well as at learning new faces. A substantial proportion of right MTLE patients (33%) even improved their response times on several tasks, which may indicate a functional release of visuo-spatial processing after resection in the right ATL. Altogether this study shows that face recognition abilities are mainly unaffected by ATL resection in MTLE, either because the critical regions for face recognition are spared or because performance at some tasks is already lower than normal preoperatively. Overall, these findings urge caution when interpreting the causal effect of brain lesions on face recognition ability in patients with ATL resection due to MTLE. They also illustrate the complexity of predicting cognitive outcomes after epilepsy surgery because of the influence of many different intertwined factors.

Radial bias in face identification

Human vision in the periphery is most accurate for stimuli that point towards the fovea. This so-called radial bias has been linked with the organization and spatial selectivity of neurons at the lowest levels of the visual system, from retinal ganglion cells onwards. Despite evidence that the human visual system is radially biased, it is not yet known whether this bias persists at higher levels of processing, or whether high-level representations are invariant to this low-level orientation bias. We used the case of face identity recognition to address this question. The specialized high-level mechanisms that support efficient face recognition are highly dependent on horizontally oriented information, which convey the most useful identity cues in the fovea. We show that face selective mechanisms are more sensitive on the horizontal meridian (to the left and right of fixation) compared to the vertical meridian (above and below fixation), suggesting that the horizontal cues in the face are better extracted on the horizontal meridian, where they align with the radial bias. The results demonstrate that the radial bias is maintained at high-level recognition stages and emphasize the importance of accounting for the radial bias in future investigation of visual recognition processes in peripheral vision.

Intracranial Electroencephalography and Deep Neural Networks Reveal Shared Substrates for Representations of Face Identity and Expressions.

According to a classical view of face perception (Bruce and Young, 1986; Haxby et al., 2000), face identity and facial expression recognition are performed by separate neural substrates (ventral and lateral temporal face-selective regions, respectively). However, recent studies challenge this view, showing that expression valence can also be decoded from ventral regions (Skerry and Saxe, 2014; Li et al., 2019), and identity from lateral regions (Anzellotti and Caramazza, 2017). These findings could be reconciled with the classical view if regions specialized for one task (either identity or expression) contain a small amount of information for the other task (that enables above-chance decoding). In this case, we would expect representations in lateral regions to be more similar to representations in deep convolutional neural networks (DCNNs) trained to recognize facial expression than to representations in DCNNs trained to recognize face identity (the converse should hold for ventral regions). We tested this hypothesis by analyzing neural responses to faces varying in identity and expression. Representational dissimilarity matrices (RDMs) computed from human intracranial recordings (n = 11 adults; 7 females) were compared with RDMs from DCNNs trained to label either identity or expression. We found that RDMs from DCNNs trained to recognize identity correlated with intracranial recordings more strongly in all regions tested-even in regions classically hypothesized to be specialized for expression. These results deviate from the classical view, suggesting that face-selective ventral and lateral regions contribute to the representation of both identity and expression.SIGNIFICANCE STATEMENT Previous work proposed that separate brain regions are specialized for the recognition of face identity and facial expression. However, identity and expression recognition mechanisms might share common brain regions instead. We tested these alternatives using deep neural networks and intracranial recordings from face-selective brain regions. Deep neural networks trained to recognize identity and networks trained to recognize expression learned representations that correlate with neural recordings. Identity-trained representations correlated with intracranial recordings more strongly in all regions tested, including regions hypothesized to be expression specialized in the classical hypothesis. These findings support the view that identity and expression recognition rely on common brain regions. This discovery may require reevaluation of the roles that the ventral and lateral neural pathways play in processing socially relevant stimuli.

Autism Spectrum Disorder and Face Identity Recognition Deficit across Ages

The purpose of this review was to assess the face identity recognition deficit and the developmental difference that manifested in autism spectrum disorder (ASD) compared to their typically developing (TD) peers. Based on the meta-analysis using a random-effect model of 94 studies, with 144 effect sizes, for both adult and pediatric subjects with simultaneous and delayed face identity recognition paradigms, the underperformance in ASD was significant and persistent across ages. In addition, a higher level of deficit was found in adult ASD when performing simultaneous face-matching tasks while other subgroups showed homogenous effect sizes. This suggested a dissociation between the difficulties of the two mechanisms of face recognition: face perception (perceiving identity from the face with minimal memory load required) and face memory (recall of identity from the face that requires memory load), which was only shown in adults but not in children. The result indicated the possibility of using face identity recognition deficit as a diagnostic trait for ASD.

Investigating the impact of disposable surgical face-masks on face identity and emotion recognition in adults with autism spectrum disorder.

With the outburst of the COVID-19 pandemic, disposable surgical face-masks (DSFMs) have been widely adopted as a preventive measure. DSFMs hide the bottom half of the face, thus making identity and emotion recognition very challenging, both in typical and atypical populations. Individuals with autism spectrum disorder (ASD) are often characterized by face processing deficits; thus, DSFMs could pose even a greater challenge for this population compared to typically development (TD) individuals. In this study, 48 ASDs of level 1 and 110 TDs underwent two tasks: (i) the Old-new face memory task, which assesses whether DSFMs affect face learning and recognition, and (ii) the Facial affect task, which explores DSFMs' effect on emotion recognition. Results from the former show that, when faces were learned without DSFMs, identity recognition of masked faces decreased for both ASDs and TDs. In contrast, when faces were first learned with DSFMs, TDs but not ASDs benefited from a "context congruence" effect, that is, faces wearing DSFMs were better recognized if learned wearing DSFMs. In addition, results from the Facial affect task show that DSFMs negatively impacted specific emotion recognition in both TDs and ASDs, although differentially between the two groups. DSFMs negatively affected disgust, happiness and sadness recognition in TDs; in contrast, ASDs performance decreased for every emotion except anger. Overall, our study demonstrates a general, although different, disruptive effect on identity and emotion recognition both in ASD and TD population.

Pupil dilation during encoding, but not type of auditory stimulation, predicts recognition success in face memory

We encounter and process information from multiple sensory modalities in our daily lives, and research suggests that learning can be more efficient when contexts are multisensory. In this study, we were interested in whether face identity recognition memory might be improved in multisensory learning conditions, and to explore associated changes in pupil dilation during encoding and recognition. In two studies participants completed old/new face recognition tasks wherein visual face stimuli were presented in the context of sounds. Faces were learnt alongside no sound, low arousal sounds (Experiment 1), high arousal non-face relevant, or high arousal face relevant (Experiment 2) sounds. We predicted that the presence of sounds during encoding would improve later recognition accuracy, however, the results did not support this with no effect of sound condition on memory. Pupil dilation, however, was found to predict later successful recognition both at encoding and during recognition. While these results do not provide support to the notion that face learning is improved under multisensory conditions relative to unisensory conditions, they do suggest that pupillometry may be a useful tool to further explore face identity learning and recognition.