scholarly journals The contribution of general object recognition abilities to face recognition

2012 ◽  
Vol 12 (9) ◽  
pp. 810-810 ◽  
Author(s):  
R. McGugin ◽  
J. Richler ◽  
G. Herzmann ◽  
M. Speegle ◽  
I. Gauthier
2015 ◽  
Vol 112 (41) ◽  
pp. 12887-12892 ◽  
Author(s):  
Nicholas G. Shakeshaft ◽  
Robert Plomin

Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.


2005 ◽  
Vol 47 (4) ◽  
Author(s):  
Rolf P. Würtz

SummuryAutomatic face recognition is a slowly maturing and economically highly important technology, which still falls short of the high expectations set on it. The variation in images taken from the same person makes face recognition systems difficult to design — it is impossible to explicitly code all variability. Successful systems have relied heavily on the principle of self-organizing many fragile cues to arrive at a robust decision and have been built by learning from biological systems. The paper describes the techniques of elastic bunch graph matching as a hierarchical integration of image pixels into Gabor responses, jets, graphs, and bunch graphs. Beside face recognition, these concepts are used for face classification learned from examples. It is attempted to develop them further to reach a complete parameterization of all faces. Methods for more general object recognition using the same Organic Computing principles are outlined and include the concept of end-stopped cells as corner detectors.


2019 ◽  
Vol 35 (05) ◽  
pp. 525-533
Author(s):  
Evrim Gülbetekin ◽  
Seda Bayraktar ◽  
Özlenen Özkan ◽  
Hilmi Uysal ◽  
Ömer Özkan

AbstractThe authors tested face discrimination, face recognition, object discrimination, and object recognition in two face transplantation patients (FTPs) who had facial injury since infancy, a patient who had a facial surgery due to a recent wound, and two control subjects. In Experiment 1, the authors showed them original faces and morphed forms of those faces and asked them to rate the similarity between the two. In Experiment 2, they showed old, new, and implicit faces and asked whether they recognized them or not. In Experiment 3, they showed them original objects and morphed forms of those objects and asked them to rate the similarity between the two. In Experiment 4, they showed old, new, and implicit objects and asked whether they recognized them or not. Object discrimination and object recognition performance did not differ between the FTPs and the controls. However, the face discrimination performance of FTP2 and face recognition performance of the FTP1 were poorer than that of the controls were. Therefore, the authors concluded that the structure of the face might affect face processing.


2013 ◽  
Vol 46 (12) ◽  
pp. 3300-3314 ◽  
Author(s):  
Kirt Lillywhite ◽  
Dah-Jye Lee ◽  
Beau Tippetts ◽  
James Archibald

Cognition ◽  
2017 ◽  
Vol 166 ◽  
pp. 42-55 ◽  
Author(s):  
Jennifer J. Richler ◽  
Jeremy B. Wilmer ◽  
Isabel Gauthier

Author(s):  
Charles A. Collin ◽  
Chang Hong Liu ◽  
Nikolaus F. Troje ◽  
Patricia A. McMullen ◽  
Avi Chaudhuri

1997 ◽  
Vol 9 (5) ◽  
pp. 555-604 ◽  
Author(s):  
Morris Moscovitch ◽  
Gordon Winocur ◽  
Marlene Behrmann

In order to study face recognition in relative isolation from visual processes that may also contribute to object recognition and reading, we investigated CK, a man with normal face recognition but with object agnosia and dyslexia caused by a closed-head injury. We administered recognition tests of up right faces, of family resemblance, of age-transformed faces, of caricatures, of cartoons, of inverted faces, and of face features, of disguised faces, of perceptually degraded faces, of fractured faces, of faces parts, and of faces whose parts were made of objects. We compared CK's performance with that of at least 12 control participants. We found that CK performed as well as controls as long as the face was upright and retained the configurational integrity among the internal facial features, the eyes, nose, and mouth. This held regardless of whether the face was disguised or degraded and whether the face was represented as a photo, a caricature, a cartoon, or a face composed of objects. In the last case, CK perceived the face but, unlike controls, was rarely aware that it was composed of objects. When the face, or just the internal features, were inverted or when the configurational gestalt was broken by fracturing the face or misaligning the top and bottom halves, CK's performance suffered far more than that of controls. We conclude that face recognition normally depends on two systems: (1) a holistic, face-specific system that is dependent on orientationspecific coding of second-order relational features (internal), which is intact in CK and (2) a part-based object-recognition system, which is damaged in CK and which contributes to face recognition when the face stimulus does not satisfy the domain-specific conditions needed to activate the face system.


2019 ◽  
Author(s):  
Jeroen van Paridon ◽  
Markus Ostarek ◽  
Mrudula Arunkumar ◽  
Falk Huettig

Human cultural inventions, such as written language, are far too recent for dedicated neural infrastructure to have evolved in its service. Culturally newly acquired skills (e.g. reading) thus ‘recycle’ evolutionarily older circuits that originally evolved for different, but similar functions (e.g. visual object recognition). The destructive competition hypothesis predicts that this neuronal recycling has detrimental effects on the cognitive functions a cortical network originally evolved for. The converse possibility is that learning to read fine-tunes general object recognition mechanisms, resulting in improved recognition across categories. In a large-scale behavioral study with literate, low-literate, and illiterate participants from the same socioeconomic background we find that even after adjusting for cognitive ability and test-taking familiarity, literacy is associated with an increase, rather than a decrease, in object recognition abilities across object categories. These results are incompatible with the claim that neuronal recycling results in destructive competition.


Author(s):  
Anibal Pedraza ◽  
Oscar Deniz ◽  
Gloria Bueno

AbstractThe phenomenon of Adversarial Examples has become one of the most intriguing topics associated to deep learning. The so-called adversarial attacks have the ability to fool deep neural networks with inappreciable perturbations. While the effect is striking, it has been suggested that such carefully selected injected noise does not necessarily appear in real-world scenarios. In contrast to this, some authors have looked for ways to generate adversarial noise in physical scenarios (traffic signs, shirts, etc.), thus showing that attackers can indeed fool the networks. In this paper we go beyond that and show that adversarial examples also appear in the real-world without any attacker or maliciously selected noise involved. We show this by using images from tasks related to microscopy and also general object recognition with the well-known ImageNet dataset. A comparison between these natural and the artificially generated adversarial examples is performed using distance metrics and image quality metrics. We also show that the natural adversarial examples are in fact at a higher distance from the originals that in the case of artificially generated adversarial examples.


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