Identity Verification for Attendees of Large-Scale Events Using Face Recognition of Selfies Taken with Smartphone Cameras

2018 ◽  
pp. 501-509 ◽  
Author(s):  
Akitoshi Okumura ◽  
Takamichi Hoshino ◽  
Susumu Handa ◽  
Eiko Yamada ◽  
Masahiro Tabuchi
Keyword(s):  
Face Recognition ◽  
Large Scale ◽  
Identity Verification

scholarly journals Identity Verification of Ticket Holders at Large-scale Events Using Face Recognition

2017 ◽  
Vol 25 (0) ◽  
pp. 448-458 ◽  
Author(s):  
Akitoshi Okumura ◽  
Takamichi Hoshino ◽  
Susumu Handa ◽  
Yugo Nishiyama ◽  
Masahiro Tabuchi
Keyword(s):  
Face Recognition ◽  
Large Scale ◽  
Identity Verification

scholarly journals Identity Verification for Attendees of Large-scale Events Using Face Recognition of Selfies Taken with Smartphone Cameras

2018 ◽  
Vol 26 (0) ◽  
pp. 779-788 ◽  
Author(s):  
Akitoshi Okumura ◽  
Takamichi Hoshino ◽  
Susumu Handa ◽  
Eiko Yamada ◽  
Masahiro Tabuchi
Keyword(s):  
Face Recognition ◽  
Large Scale ◽  
Identity Verification

Large scale database face recognition based on region constriction

2013 ◽  
Vol 32 (7) ◽  
pp. 2049-2052
Author(s):  
Chao-you LI ◽  
Ji-zhou SUN
Keyword(s):  
Face Recognition ◽  
Large Scale

scholarly journals Illumination Normalization Algorithm Combination Analysis in Face Recognition

2017 ◽  
Vol 9 (3) ◽  
pp. 334-339
Author(s):  
Rokas Semėnas
Keyword(s):  
Face Recognition ◽  
Large Scale ◽  
Real Life ◽  
Direct Approach ◽  
Small Scale ◽  
Test Results ◽  
Illumination Normalization ◽  
Recognition Algorithms ◽  
Life Problems

Face recognition programs have many practical usages in various fields, such as security or entertainment. Existing recognition algorithms must deal with various real life problems – mainly with illumination. In practice, illumination normalization models are often used only for Small-scale futures extraction, ignoring Large-scale features. In this article, new and more direct approach to this problem is offered, used algorithms and test results are given.

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.

scholarly journals Face Perception Deficits in Developmental Prosopagnosia

2021 ◽  
Author(s):  
◽  
Ella Macaskill
Keyword(s):  
Face Recognition ◽  
Face Perception ◽  
Test Performance ◽  
Large Scale ◽  
Memory Deficits ◽  
Face Memory ◽  
The Face ◽  
Component Processes ◽  
Severe Difficulty ◽  
Scale Experiment

<p>Face recognition is a fundamental cognitive function that is essential for social interaction – yet not everyone has it. Developmental prosopagnosia is a lifelong condition in which people have severe difficulty recognising faces but have normal intellect and no brain damage. Despite much research, the component processes of face recognition that are impaired in developmental prosopagnosia are not well understood. Two core processes are face perception, being the formation of visual representations of a currently seen face, and face memory, being the storage, maintenance, and retrieval of those representations. Most studies of developmental prosopagnosia focus on face memory deficits, but a few recent studies indicate that face perception deficits might also be important. Characterising face perception in developmental prosopagnosia is crucial for a better understanding of the condition. In this thesis, I addressed this issue in a large-scale experiment with 108 developmental prosopagnosics and 136 matched controls. I assessed face perception abilities with multiple measures and ran a broad range of analyses to establish the severity, scope, and nature of face perception deficits in developmental prosopagnosia. Three major results stand out. First, face perception deficits in developmental prosopagnosia were severe, and could be comparable in size to face memory deficits. Second, the face perception deficits were widespread, affecting the whole sample rather than a subset of individuals. Third, the deficits were mainly driven by impairments to mechanisms specialised for processing upright faces. Further analyses revealed several other features of the deficits, including the use of atypical and inconsistent strategies for perceiving faces, difficulties matching the same face across different pictures, equivalent impact of lighting and viewpoint variations in face images, and atypical perceptual and non-perceptual components of test performance. Overall, my thesis shows that face perception deficits are more central to developmental prosopagnosia than previously thought and motivates further research on the issue.</p>

scholarly journals MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

2016 ◽  
pp. 87-102 ◽  
Author(s):  
Yandong Guo ◽  
Lei Zhang ◽  
Yuxiao Hu ◽  
Xiaodong He ◽  
Jianfeng Gao
Keyword(s):  
Face Recognition ◽  
Large Scale

Automatic Face Image Tagging in Large Collections

2014 ◽  
pp. 336-358
Author(s):  
Silvio Barra ◽  
Maria De Marsico ◽  
Chiara Galdi
Keyword(s):  
Social Networks ◽  
Face Recognition ◽  
Large Scale ◽  
Face Image ◽  
Image Tagging ◽  
Indexing And Retrieval ◽  
Magazine Publishing ◽  
Face Datasets ◽  
Traditional Approaches ◽  
Personal Devices

In this chapter, the authors present some issues related to automatic face image tagging techniques. Their main purpose in user applications is to support the organization (indexing) and retrieval (or easy browsing) of images or videos in large collections. Their core modules include algorithms and strategies for handling very large face databases, mostly acquired in real conditions. As a background for understanding how automatic face tagging works, an overview about face recognition techniques is given, including both traditional approaches and novel proposed techniques for face recognition in uncontrolled settings. Moreover, some applications and the way they work are summarized, in order to depict the state of the art in this area of face recognition research. Actually, many of them are used to tag faces and to organize photo albums with respect to the person(s) presented in annotated photos. This kind of activity has recently expanded from personal devices to social networks, and can also significantly support more demanding tasks, such as automatic handling of large editorial collections for magazine publishing and archiving. Finally, a number of approaches to large-scale face datasets as well as some automatic face image tagging techniques are presented and compared. The authors show that many approaches, both in commercial and research applications, still provide only a semi-automatic solution for this problem.

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