Self- or familiar-face recognition advantage? New insight using ambient images

2018 ◽  
Vol 71 (6) ◽  
pp. 1396-1404 ◽  
Author(s):  
Catherine Bortolon ◽  
Siméon Lorieux ◽  
Stéphane Raffard
Keyword(s):  
Face Recognition ◽  
Graduate Students ◽  
Matching Task ◽  
Healthy Individuals ◽  
Familiarity Effect ◽  
Familiar Face ◽  
The Past ◽  
The Face ◽  
First Time ◽  
Unfamiliar Faces

Self-face recognition has been widely explored in the past few years. Nevertheless, the current literature relies on the use of standardized photographs which do not represent daily-life face recognition. Therefore, we aim for the first time to evaluate self-face processing in healthy individuals using natural/ambient images which contain variations in the environment and in the face itself. In total, 40 undergraduate and graduate students performed a forced delayed-matching task, including images of one’s own face, friend, famous and unknown individuals. For both reaction time and accuracy, results showed that participants were faster and more accurate when matching different images of their own face compared to both famous and unfamiliar faces. Nevertheless, no significant differences were found between self-face and friend-face and between friend-face and famous-face. They were also faster and more accurate when matching friend and famous faces compared to unfamiliar faces. Our results suggest that faster and more accurate responses to self-face might be better explained by a familiarity effect – that is, (1) the result of frequent exposition to one’s own image through mirror and photos, (2) a more robust mental representation of one’s own face and (3) strong face recognition units as for other familiar faces.

scholarly journals How Familiarity Warps Representation in Face Space

2018 ◽  
Author(s):  
Vassiki Chauhan ◽  
M. Ida Gobbini
Keyword(s):  
Face Recognition ◽  
Structural Features ◽  
Image Distortion ◽  
Face Space ◽  
Familiar Face ◽  
Unfamiliar Faces

AbstractRecognition of familiar as compared to unfamiliar faces is robust and resistant to marked image distortion or degradation. Here we tested the flexibility of familiar face recognition with a morphing paradigm where the appearance of a personally familiar face was mixed with the appearance of a stranger. The aim was to assess how categorical boundaries for recognition of identity are affected by familiarity. We found a narrower categorical boundary for the identity of personally familiar faces when they were mixed with unfamiliar identities as compared to the control condition, in which the appearance of two unfamiliar faces were mixed. Our results suggest that familiarity warps the representational geometry of face space, amplifying perceptual distances for small changes in the appearance of familiar faces that are inconsistent with the structural features that define their identities.

scholarly journals Critical Features for Face Recognition

2018 ◽  
Author(s):  
Naphtali Abudarham ◽  
Lior Shkiller ◽  
Galit Yovel
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Deep Neural Network ◽  
Recognition Algorithm ◽  
Facial Features ◽  
Perceptual Representation ◽  
Familiar Face ◽  
Perceptual Representations ◽  
Unfamiliar Faces ◽  
New Framework

Face recognition is a computationally challenging task that humans perform effortlessly. Nonetheless, this remarkable ability is limited to familiar faces and does not generalize to unfamiliar faces. To account for humans’ superior ability to recognize familiar faces, current theories suggest that familiar and unfamiliar faces have different perceptual representations. In the current study, we applied a reverse engineering approach to reveal which facial features are critical for familiar face recognition. In contrast to current views, we discovered that the same subset of features that are used for matching unfamiliar faces, are also used for matching as well as recognition of familiar faces. We further show that these features are also used by a deep neural network face recognition algorithm. We therefore propose a new framework that assumes similar perceptual representation for all faces and integrates cognition and perception to account for humans’ superior recognition of familiar faces.

scholarly journals How robust is familiar face recognition? A repeat detection study of more than 1000 faces

2018 ◽  
Vol 5 (5) ◽  
pp. 170634
Author(s):  
Angus F. Chapman ◽  
Hannah Hawkins-Elder ◽  
Tirta Susilo
Keyword(s):  
Face Recognition ◽  
Detection Task ◽  
Familiar Face ◽  
Robust Representation ◽  
Face Images ◽  
Unfamiliar Face ◽  
Unfamiliar Faces ◽  
Repeat Detection ◽  
Better Than

Recent theories suggest that familiar faces have a robust representation in memory because they have been encountered over a wide variety of contexts and image changes (e.g. lighting, viewpoint and expression). By contrast, unfamiliar faces are encountered only once, and so they do not benefit from such richness of experience and are represented based on image-specific details. In this registered report, we used a repeat detection task to test whether familiar faces are recognized better than unfamiliar faces across image changes. Participants viewed a stream of more than 1000 celebrity face images for 0.5 s each, any of which might be repeated at a later point and has to be detected. Some participants saw the same image at repeats, while others saw a different image of the same face. A post-experimental familiarity check allowed us to determine which celebrities were and were not familiar to each participant. We had three predictions: (i) detection would be better for familiar than unfamiliar faces, (ii) detection would be better across same rather than different images, and (iii) detection of familiar faces would be comparable across same and different images, but detection of unfamiliar faces would be poorer across different images. We obtained support for the first two predictions but not the last. Instead, we found that repeat detection of faces, regardless of familiarity, was poorer across different images. Our study suggests that the robustness of familiar face recognition may have limits, and that under some conditions, familiar face recognition can be just as influenced by image changes as unfamiliar face recognition.

scholarly journals An objective, sensitive and ecologically valid neural measure of rapid human individual face recognition

2019 ◽  
Vol 6 (6) ◽  
pp. 181904 ◽  
Author(s):  
Friederike G. S. Zimmermann ◽  
Xiaoqian Yan ◽  
Bruno Rossion
Keyword(s):  
Face Recognition ◽  
Visual Stimulation ◽  
Natural Images ◽  
Neural Basis ◽  
Face Identity ◽  
Familiar Face ◽  
Recognition Response ◽  
Human Individual ◽  
The Individual ◽  
Unfamiliar Faces

Humans may be the only species able to rapidly and automatically recognize a familiar face identity in a crowd of unfamiliar faces, an important social skill. Here, by combining electroencephalography (EEG) and fast periodic visual stimulation (FPVS), we introduce an ecologically valid, objective and sensitive neural measure of this human individual face recognition function. Natural images of various unfamiliar faces are presented at a fast rate of 6 Hz, allowing one fixation per face, with variable natural images of a highly familiar face identity, a celebrity, appearing every seven images (0.86 Hz). Following a few minutes of stimulation, a high signal-to-noise ratio neural response reflecting the generalized discrimination of the familiar face identity from unfamiliar faces is observed over the occipito-temporal cortex at 0.86 Hz and harmonics. When face images are presented upside-down, the individual familiar face recognition response is negligible, being reduced by a factor of 5 over occipito-temporal regions. Differences in the magnitude of the individual face recognition response across different familiar face identities suggest that factors such as exposure, within-person variability and distinctiveness mediate this response. Our findings of a biological marker for fast and automatic recognition of individual familiar faces with ecological stimuli open an avenue for understanding this function, its development and neural basis in neurotypical individual brains along with its pathology. This should also have implications for the use of facial recognition measures in forensic science.

scholarly journals Surgical face masks impair human face matching performance for familiar and unfamiliar faces

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Daniel J. Carragher ◽  
Peter J. B. Hancock
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Human Face ◽  
Face Matching ◽  
Face Recognition System ◽  
Surgical Masks ◽  
The Face ◽  
Matching Performance ◽  
Recognition Systems ◽  
Unfamiliar Faces

AbstractIn response to the COVID-19 pandemic, many governments around the world now recommend, or require, that their citizens cover the lower half of their face in public. Consequently, many people now wear surgical face masks in public. We investigated whether surgical face masks affected the performance of human observers, and a state-of-the-art face recognition system, on tasks of perceptual face matching. Participants judged whether two simultaneously presented face photographs showed the same person or two different people. We superimposed images of surgical masks over the faces, creating three different mask conditions: control (no masks), mixed (one face wearing a mask), and masked (both faces wearing masks). We found that surgical face masks have a large detrimental effect on human face matching performance, and that the degree of impairment is the same regardless of whether one or both faces in each pair are masked. Surprisingly, this impairment is similar in size for both familiar and unfamiliar faces. When matching masked faces, human observers are biased to reject unfamiliar faces as “mismatches” and to accept familiar faces as “matches”. Finally, the face recognition system showed very high classification accuracy for control and masked stimuli, even though it had not been trained to recognise masked faces. However, accuracy fell markedly when one face was masked and the other was not. Our findings demonstrate that surgical face masks impair the ability of humans, and naïve face recognition systems, to perform perceptual face matching tasks. Identification decisions for masked faces should be treated with caution.

scholarly journals Real-World Face Recognition: The Importance of Surface Reflectance Properties

Perception ◽  
10.1068/p5779 ◽  
2007 ◽  
Vol 36 (9) ◽  
pp. 1368-1374 ◽  
Author(s):  
Richard Russell ◽  
Pawan Sinha
Keyword(s):  
Face Recognition ◽  
Real World ◽  
Recognition Task ◽  
Color Images ◽  
Surface Reflectance ◽  
Shape Information ◽  
Familiar Face ◽  
The Face ◽  
Unfamiliar Face ◽  
Facial Attributes

The face recognition task we perform most often in everyday experience is the identification of people with whom we are familiar. However, because of logistical challenges, most studies focus on unfamiliar-face recognition, wherein subjects are asked to match or remember images of unfamiliar people's faces. Here we explore the importance of two facial attributes—shape and surface reflectance—in the context of a familiar-face recognition task. In our experiment, subjects were asked to recognise color images of the faces of their friends. The images were manipulated such that only reflectance or only shape information was useful for recognizing any particular face. Subjects were actually better at recognizing their friends' faces from reflectance information than from shape information. This provides evidence that reflectance information is important for face recognition in ecologically relevant contexts.

scholarly journals Face Recognition Technology Based on Partial Facial Features

2021 ◽  
Vol 37 (5) ◽  
pp. 292-297
Author(s):  
Winney Eva
Keyword(s):  
Face Recognition ◽  
Facial Features ◽  
The Past ◽  
Key Points ◽  
The Face ◽  
Full Face ◽  
Human Faces

In the past two decades, many face recognition methods have been proposed. Among them, most researchers use the entire face as the basis for recognition. The basic technical route is to extract and compare the general features of the entire face. However, in actual scenes, human faces may be blocked by obstacles. Therefore, how to realize face recognition by using some of the facial features that can be obtained? In addition, this partial face recognition technology is mostly based on the acquisition of key points of the face to recognize the whole face. This review intends to summarize the full face and partial face recognition methods based on key points of the face.

scholarly journals Compressed representation of natural image variability underlies high accuracy in face recognition

2021 ◽  
Author(s):  
David White ◽  
Tanya Wayne ◽  
Victor Perrone de Lima Varela
Keyword(s):  
Face Recognition ◽  
Computational Analysis ◽  
High Accuracy ◽  
Natural Image ◽  
Cognitive Representations ◽  
Human Face Recognition ◽  
Familiar Face ◽  
Similarity Ratings ◽  
Image Averaging ◽  
Unfamiliar Faces

Accurately recognising faces is fundamental to human social interaction. 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. Using image averaging, and inspired by the computation of Analysis of Variance, we partitioned image variation into differences between faces (between-identity variation) and differences between photos of the same face (within-identity variation). 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, similarity of within-identity variation was compressed for familiar faces relative to unfamiliar faces. Second, viewers that are extremely accurate in face recognition – ‘super-recognisers’ – showed enhanced compression of within-identity variation that was most marked for familiar faces. We also present computational analysis showing that cognitive transformations of between- and within-identity variation make separable contributions to perceptual expertise in unfamiliar and familiar face identification respectively. We conclude that inter- and intra-individual accuracy differences primarily arise from differences in the representation of familiar face image variation.

scholarly journals Audio and Visual Model for Real World Interaction

2021 ◽  
Vol 9 (VI) ◽  
pp. 635-639
Author(s):  
Akarshak Bose
Keyword(s):  
Face Recognition ◽  
Real World ◽  
Voice Recognition ◽  
Recognition System ◽  
Full Information ◽  
Visual Model ◽  
The Past ◽  
The Face ◽  
Stored Information ◽  
Recognition Technique

: Communication with the proper information can be helpful for any person to carry out conversations. The proposed system is to help people to interact freely with full information about the past conversations with the person they are meeting. The device UPAL will identify the face and voice of the person and will store necessary details about the meeting, by recording the conversations or by taking inputs from the user. Next time when the user meets the same person, the device will fetch the information from the storage that can be used for a comfortable conversation. UPAL is made up of a Camera and microphone that will use the Face recognition technique and voice recognition system to collect the data. A mobile-based application will be provided to the user for viewing, editing the stored information. UPAL will ensure smart conversation by guiding and reminding the user.

Coarse-to-Fine(r) Automatic Familiar Face Recognition in the Human Brain

2021 ◽  
Author(s):  
Xiaoqian Yan ◽  
Valérie Goffaux ◽  
Bruno Rossion
Keyword(s):  
Face Recognition ◽  
Human Brain ◽  
Spatial Resolution ◽  
Predictive Coding ◽  
Head Width ◽  
Familiar Face ◽  
Visual Inputs ◽  
Electroencephalogram Eeg ◽  
Coarse To Fine ◽  
Unfamiliar Faces

Abstract At what level of spatial resolution can the human brain recognize a familiar face in a crowd of strangers? Does it depend on whether one approaches or rather moves back from the crowd? To answer these questions, 16 observers viewed different unsegmented images of unfamiliar faces alternating at 6 Hz, with spatial frequency (SF) content progressively increasing (i.e., coarse-to-fine) or decreasing (fine-to-coarse) in different sequences. Variable natural images of celebrity faces every sixth stimulus generated an objective neural index of single-glanced automatic familiar face recognition (FFR) at 1 Hz in participants’ electroencephalogram (EEG). For blurry images increasing in spatial resolution, the neural FFR response over occipitotemporal regions emerged abruptly with additional cues at about 6.3–8.7 cycles/head width, immediately reaching amplitude saturation. When the same images progressively decreased in resolution, the FFR response disappeared already below 12 cycles/head width, thus providing no support for a predictive coding hypothesis. Overall, these observations indicate that rapid automatic recognition of heterogenous natural views of familiar faces is achieved from coarser visual inputs than generally thought, and support a coarse-to-fine FFR dynamics in the human brain.

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