scholarly journals Recognising Familiar Faces Out of Context

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

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.

Perception ◽  
2017 ◽  
Vol 47 (2) ◽  
pp. 185-196 ◽  
Author(s):  
Marlena L. Itz ◽  
Stefan R. Schweinberger ◽  
Jürgen M. Kaufmann

The role of second-order configuration—that is, metric distances between individual features—for familiar face recognition has been the subject of debate. Recent reports suggest that better face recognition abilities coincide with a weaker reliance on shape information for face recognition. We examined contributions of second-order configuration to familiar face repetition priming by manipulating metric distances between facial features. S1 comprised familiar face primes as either: unaltered, with increased or decreased interocular distance, with increased or decreased distance between nose and mouth; or a different familiar face (unprimed). Participants performed a familiarity decision task on familiar and unfamiliar S2 targets, and completed a test battery consisting of three face identity processing tests. Accuracies, reaction times, and inverse efficiency scores were assessed for the priming experiment, and potential priming costs in inverse efficiency scores were correlated with test battery scores. Overall, priming was found, and priming effects were reduced only by primes with interocular distance distortions. Correlational data showed that better face recognition skills coincided with a weaker reliance on second-order configurations. Our findings (a) suggest an importance of interocular, but not mouth-to-nose, distances for familiar face recognition and (b) show that good face recognizers are less sensitive to second-order configuration.


2018 ◽  
Author(s):  
Jeesun Kim ◽  
Sonya Karisma ◽  
Vincent Aubanel ◽  
Chris Davis

Perception ◽  
10.1068/p5192 ◽  
2005 ◽  
Vol 34 (9) ◽  
pp. 1117-1134 ◽  
Author(s):  
Claus-Christian Carbon ◽  
Helmut Leder

We investigated the early stages of face recognition and the role of featural and holistic face information. We exploited the fact that, on inversion, the alienating disorientation of the eyes and mouth in thatcherised faces is hardly detectable. This effect allows featural and holistic information to be dissociated and was used to test specific face-processing hypotheses. In inverted thatcherised faces, the cardinal features are already correctly oriented, whereas in undistorted faces, the whole Gestalt is coherent but all information is disoriented. Experiment 1 and experiment 3 revealed that, for inverted faces, featural information processing precedes holistic information. Moreover, the processing of contextual information is necessary to process local featural information within a short presentation time (26 ms). Furthermore, for upright faces, holistic information seems to be available faster than for inverted faces (experiment 2). These differences in processing inverted and upright faces presumably cause the differential importance of featural and holistic information for inverted and upright faces.


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

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 699 ◽  
Author(s):  
Carmen Moret-Tatay ◽  
Inmaculada Baixauli-Fortea ◽  
M. Dolores Grau Sevilla ◽  
Tatiana Quarti Irigaray

Face recognition is located in the fusiform gyrus, which is also related to other tasks such word recognition. Although these two processes have several similarities, there are remarkable differences that include a vast range of approaches, which results from different groups of participants. This research aims to examine how the word-processing system processes faces at different moments and vice versa. Two experiments were carried out. Experiment 1 allowed us to examine the classical discrimination task, while Experiment 2 allowed us to examine very early moments of discrimination. In the first experiment, 20 Spanish University students volunteered to participate. Secondly, a sample of 60 participants from different nationalities volunteered to take part in Experiment 2. Furthermore, the role of sex and place of origin were considered in Experiment 1. No differences between men and women were found in Experiment 1, nor between conditions. However, Experiment 2 depicted shorter latencies for faces than word names, as well as a higher masked repetition priming effect for word identities and word names preceded by faces. Emerging methodologies in the field might help us to better understand the relationship among these two processes. For this reason, a network analysis approach was carried out, depicting sub-communities of nodes related to face or word name recognition, which were replicated across different groups of participants. Bootstrap inferences are proposed to account for variability in estimating the probabilities in the current samples. This supports that both processes are related to early moments of recognition, and rather than being independent, they might be bilaterally distributed with some expert specializations or preferences.


2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sajid ◽  
Nouman Ali ◽  
Saadat Hanif Dar ◽  
Naeem Iqbal Ratyal ◽  
Asif Raza Butt ◽  
...  

Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging. Existing face recognition methods rely on feature extraction and reference reranking to improve the performance. However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult. The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces. The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends. To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations. The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup. We also evaluate the role of partial and full makeup in face images to improve the recognition performance. The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.


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