Cognitive mechanisms of face processing

1992 ◽  
Vol 335 (1273) ◽  
pp. 113-119 ◽  
Keyword(s):  
Face Recognition ◽  
Repetition Priming ◽  
Semantic Knowledge ◽  
Recent Past ◽  
Cognitive Mechanisms ◽  
Cognitive Representations ◽  
Familiar Face ◽  
The Face ◽  
Related Individuals ◽  
Familiar Person

Evidence from natural and induced errors of face recognition, from the effects of different cues on resolving errors, and from the latencies to make different decisions about seen faces, all suggest that familiar face recognition involves a fixed, invariant sequence of stages. To recognize a familiar face, a perceptual description of a seen face must first activate a long-standing representation of the appearance of the face of the familiar person. ‘Semantic’ knowledge about such things as the person’s occupation and personality are accessed next, followed, in the final stage, by the name. Certain factors affect the ease of familiar face recognition. Faces seen in the recent past are recognized more readily (repetition priming), as are distinctive faces, and faces preceded by those of related individuals (associative priming). Our knowledge of these phenomena is reviewed for the light it can shed upon the mechanisms of face recognition. Four aspects of face recognition - graded similarity effects and part-to-whole completion in repetition priming, prototype extraction with simultaneous retention of information about individual exemplars, and distinctiveness effects in classification and identification - are proposed as being compatible with distributed memory accounts of cognitive representations.

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.

Repetition priming of face recognition

1987 ◽  
Vol 39 (2) ◽  
pp. 193-210 ◽  
Author(s):  
Andrew W. Ellis ◽  
Andrew W. Young ◽  
Brenda M. Flude ◽  
Dennis C. Hay
Keyword(s):  
Face Recognition ◽  
Repetition Priming ◽  
Body Shape ◽  
Prior Exposure ◽  
Partial Replication ◽  
Priming Effects ◽  
Physical Similarity ◽  
The Face

Three experiments investigating the priming of the recognition of familiar faces are reported. In Experiment 1, recognizing the face of a celebrity in an “Is this face familiar?” task was primed by exposure several minutes earlier to a different photograph of the same person, but not by exposure to the person's written name (a partial replication of Bruce and Valentine, 1985). In Experiment 2, recognizing the face of a personal acquaintance was again primed by recognizing a different photograph of their face, but not by recognizing the acquaintance from that person's body shape, clothes etc. Experiment 3 showed that maximum repetition priming is obtained from prior exposure to an identical photograph of a famous face, less from a similar photograph, and least (but still significant) from a dissimilar photograph. We argue that repetition priming is a function of the degree of physical similarity between two stimuli and that lack of priming between different stimulus types (e.g., written names and faces, or bodies and faces) may be attributable to lack of physical similarity between prime and test stimuli. Repetition priming effects may be best explained by some form of “instance-based” model such as that proposed by McClelland and Rumelhart (1985).

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 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 Neural microgenesis of personally familiar face recognition

2015 ◽  
Vol 112 (35) ◽  
pp. E4835-E4844 ◽  
Author(s):  
Meike Ramon ◽  
Luca Vizioli ◽  
Joan Liu-Shuang ◽  
Bruno Rossion
Keyword(s):  
Face Recognition ◽  
Visual Information ◽  
Medial Temporal Lobe ◽  
Temporal Cortex ◽  
Inferior Temporal Cortex ◽  
Sufficient Information ◽  
Neural Basis ◽  
Familiar Face ◽  
Spatial Frequencies ◽  
The Face

Despite a wealth of information provided by neuroimaging research, the neural basis of familiar face recognition in humans remains largely unknown. Here, we isolated the discriminative neural responses to unfamiliar and familiar faces by slowly increasing visual information (i.e., high-spatial frequencies) to progressively reveal faces of unfamiliar or personally familiar individuals. Activation in ventral occipitotemporal face-preferential regions increased with visual information, independently of long-term face familiarity. In contrast, medial temporal lobe structures (perirhinal cortex, amygdala, hippocampus) and anterior inferior temporal cortex responded abruptly when sufficient information for familiar face recognition was accumulated. These observations suggest that following detailed analysis of individual faces in core posterior areas of the face-processing network, familiar face recognition emerges categorically in medial temporal and anterior regions of the extended cortical face network.

Repetition priming between parts and wholes: Tests of a computational model of familiar face recognition

1997 ◽  
Vol 88 (4) ◽  
pp. 579-608 ◽  
Author(s):  
Andrew W. Ellis ◽  
A. Mike Burton ◽  
Andy Young ◽  
Brenda M. Flude
Keyword(s):  
Face Recognition ◽  
Computational Model ◽  
Repetition Priming ◽  
Familiar Face

Face Inversion Effect Emerges Under Critical Configural Discrepancy

2010 ◽  
Vol 69 (3) ◽  
pp. 161-167 ◽  
Author(s):  
Jisien Yang ◽  
Adrian Schwaninger
Keyword(s):  
Face Recognition ◽  
Face Processing ◽  
Underlying Mechanism ◽  
Inversion Effect ◽  
Configural Processing ◽  
Major Contributor ◽  
Face Inversion Effect ◽  
Configural Information ◽  
Face Inversion ◽  
The Face

Configural processing has been considered the major contributor to the face inversion effect (FIE) in face recognition. However, most researchers have only obtained the FIE with one specific ratio of configural alteration. It remains unclear whether the ratio of configural alteration itself can mediate the occurrence of the FIE. We aimed to clarify this issue by manipulating the configural information parametrically using six different ratios, ranging from 4% to 24%. Participants were asked to judge whether a pair of faces were entirely identical or different. The paired faces that were to be compared were presented either simultaneously (Experiment 1) or sequentially (Experiment 2). Both experiments revealed that the FIE was observed only when the ratio of configural alteration was in the intermediate range. These results indicate that even though the FIE has been frequently adopted as an index to examine the underlying mechanism of face processing, the emergence of the FIE is not robust with any configural alteration but dependent on the ratio of configural alteration.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

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