Recognizing Face Identity from Natural and Morphed Smiles

2006 ◽  
Vol 59 (5) ◽  
pp. 801-808 ◽  
Author(s):  
Karen Lander ◽  
Lewis Chuang ◽  
Lee Wickham
Keyword(s):  
Face Recognition ◽  
Nonrigid Motion ◽  
Face Identity ◽  
Familiar Face ◽  
Static Image ◽  
General Benefit ◽  
Better Than

It is easier to identify a degraded familiar face when it is shown moving (smiling, talking; nonrigid motion), than when it is displayed as a static image (Knight & Johnston, 1997; Lander, Christie, & Bruce, 1999). Here we explore the theoretical underpinnings of the moving face recognition advantage. In Experiment 1 we show that the identification of personally familiar faces when shown naturally smiling is significantly better than when the person is shown artificially smiling (morphed motion), as a single static neutral image or as a single static smiling image. In Experiment 2 we demonstrate that speeding up the motion significantly impairs the recognition of identity from natural smiles, but has little effect on morphed smiles. We conclude that the recognition advantage for face motion does not reflect a general benefit for motion, but suggests that, for familiar faces, information about their characteristic motion is stored in memory.

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 The neural dynamics of familiar face recognition

2018 ◽  
Author(s):  
Géza Gergely Ambrus ◽  
Daniel Kaiser ◽  
Radoslaw Martin Cichy ◽  
Gyula Kovács
Keyword(s):  
Face Recognition ◽  
Visual Processing ◽  
Right Hemisphere ◽  
Temporal Cortex ◽  
Real Life ◽  
Same Sex ◽  
Identity Information ◽  
Face Identity ◽  
Familiar Face ◽  
Invariant Representations

AbstractIn real-life situations, the appearance of a person’s face can vary substantially across different encounters, making face recognition a challenging task for the visual system. Recent fMRI decoding studies have suggested that face recognition is supported by identity representations located in regions of the occipito-temporal cortex. Here, we used EEG to elucidate the temporal emergence of these representations. Human participants (both sexes) viewed a set of highly variable face images of four highly familiar celebrities (two male, two female), while performing an orthogonal task. Univariate analyses of event-related EEG responses revealed a pronounced differentiation between male and female faces, but not between identities of the same sex. Using multivariate representational similarity analysis, we observed a gradual emergence of face identity representations, with an increasing degree of invariance. Face identity information emerged rapidly, starting shortly after 100ms from stimulus onset. From 400ms after onset and predominantly in the right hemisphere, identity representations showed two invariance properties: (1) they equally discriminated identities of opposite sexes and of the same sex, and (2) they were tolerant to image-based variations. These invariant representations may be a crucial prerequisite for successful face recognition in everyday situations, where the appearance of a familiar person can vary drastically.Significance StatementRecognizing the face of a friend on the street is a task we effortlessly perform in our everyday lives. However, the necessary visual processing underlying familiar face recognition is highly complex. As the appearance of a given person varies drastically between encounters, for example across viewpoints or emotional expressions, the brain needs to extract identity information that is invariant to such changes. Using multivariate analyses of EEG data, we characterize how invariant representations of face identity emerge gradually over time. After 400ms of processing, cortical representations reliably differentiated two similar identities (e.g., two famous male actors), even across a set of highly variable images. These representations may support face recognition under challenging real-life conditions.

scholarly journals Recognising Familiar Faces Out of Context

Perception ◽  
2021 ◽  
Vol 50 (2) ◽  
pp. 174-177
Author(s):  
Sarah Laurence ◽  
Jordyn Eyre ◽  
Ailsa Strathie
Keyword(s):  
Face Recognition ◽  
Recognition Test ◽  
Familiar Face

Expertise in familiar face recognition has been well-documented in several studies. Here, we examined the role of context using a surprise lecturer recognition test. Across two experiments, we found few students recognised their lecturer when they were unexpected, but accuracy was higher when the lecturer was preceded by a prompt. Our findings suggest that familiar face recognition can be poor in unexpected contexts.

Chaos Glowworm Swarm Optimization Algorithm Based on Cloud Model for Face Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2056009
Author(s):  
Guangyu Zhou ◽  
Aijia Ouyang ◽  
Yuming Xu
Keyword(s):  
Face Recognition ◽  
Cloud Model ◽  
Cuckoo Search ◽  
Global Optimum ◽  
Convergence Speed ◽  
Invasive Weed ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Better Than

To overcome the shortcomings of the basic glowworm swarm optimization (GSO) algorithm, such as low accuracy, slow convergence speed and easy to fall into local minima, chaos algorithm and cloud model algorithm are introduced to optimize the evolution mechanism of GSO, and a chaos GSO algorithm based on cloud model (CMCGSO) is proposed in the paper. The simulation results of benchmark function of global optimization show that the CMCGSO algorithm performs better than the cuckoo search (CS), invasive weed optimization (IWO), hybrid particle swarm optimization (HPSO), and chaos glowworm swarm optimization (CGSO) algorithm, and CMCGSO has the advantages of high accuracy, fast convergence speed and strong robustness to find the global optimum. Finally, the CMCGSO algorithm is used to solve the problem of face recognition, and the results are better than the methods from literatures.

scholarly journals Arguments Against a Configural Processing Account of Familiar Face Recognition

2015 ◽  
Vol 10 (4) ◽  
pp. 482-496 ◽  
Author(s):  
A. Mike Burton ◽  
Stefan R. Schweinberger ◽  
Rob Jenkins ◽  
Jürgen M. Kaufmann
Keyword(s):  
Face Recognition ◽  
Configural Processing ◽  
Familiar Face

scholarly journals Face recognition: When a nod is better than a wink

2001 ◽  
Vol 11 (16) ◽  
pp. R663-R664 ◽  
Author(s):  
Jim Stone
Keyword(s):  
Face Recognition ◽  
Better Than

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

2019 ◽  
Author(s):  
Nicholas Blauch ◽  
Marlene Behrmann ◽  
David C. Plaut
Keyword(s):  
Face Recognition ◽  
Theoretical Work ◽  
Fine Tuning ◽  
Identity Verification ◽  
High Performing ◽  
Face Identity ◽  
Unfamiliar Face ◽  
Perceptual Representations ◽  
Expert Networks ◽  
Identity Perception

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 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.

Face Recognition Using 2-D, 3-D, and Infrared: Is Multimodal Better Than Multisample?

2006 ◽  
Vol 94 (11) ◽  
pp. 2000-2012 ◽  
Author(s):  
K.W. Bowyer ◽  
K.I. Chang ◽  
P.J. Flynn ◽  
Xin Chen
Keyword(s):  
Face Recognition ◽  
Better Than
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