Labeled Faces in the Wild: A Survey

Learned-Miller, Erik; Huang, Gary B.; RoyChowdhury, Aruni; Li, Haoxiang; Hua, Gang

doi:10.1007/978-3-319-25958-1_8

Cited by 356 publications

(225 citation statements)

References 92 publications

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“…The result is close to 96% on a standard data set Labeled Faces in the Wild (Learned-Miller et al, 2016), which includes more than 13,000 face images taken from the Internet. A high result is provided by a new method of NN training: there were used triplets of pictures, which contained faces of one or different people, equally justified and taken under the same conditions.…”

Section: Introductionsupporting

confidence: 55%

Development of a Computer System for Identity Authentication Using Artificial Neural Networks

et al. 2017

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The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics' changes over time (e.g., due to aging). The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

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Section: Introductionsupporting

confidence: 55%

Development of a Computer System for Identity Authentication Using Artificial Neural Networks

et al. 2017

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“…More specifically, in 2007 the Labeled Faces "in-the-wild" (LFW) database [17,23] Due to the recent surge of deep learning, age estimation from facial images has gradually gathered increased interest in the community. As of today, various deep learning architectures have been proposed and several databases annotated with regard to the age attribute have been made publicly available.…”

Section: Introductionmentioning

confidence: 99%

AgeDB: The First Manually Collected, In-the-Wild Age Database

Moschoglou

Papaioannou

Sagonas

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

589

318

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Over the last few years, increased interest has arisen with respect to age-related tasks in the Computer Vision community. As a result, several "in-the-wild" databases annotated with respect to the age attribute became available in the literature. Nevertheless, one major drawback of these databases is that they are semi-automatically collected and annotated and thus they contain noisy labels. Therefore, the algorithms that are evaluated in such databases are prone to noisy estimates. In order to overcome such drawbacks, we present in this paper the first, to the best of knowledge, manually collected "in-the-wild" age database, dubbed AgeDB, containing images annotated with accurate to the year, noise-free labels. As demonstrated by a series of experiments utilizing state-of-the-art algorithms, this unique property renders AgeDB suitable when performing experiments on age-invariant face verification, age estimation and face age progression "in-the-wild".

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“…Labelled Faces in the Wild (LFW) [43] database is a popular dataset for studying multi-view faces in an unconstrained environment. It has recorded 13,233 foreground face images; other faces in the images being assimilated to the background.…”

Section: Availability and Quality Of Face Datasetsmentioning

confidence: 99%

Automated Face Recognition: Challenges and Solutions

Olszewska¹

2016

Pattern Recognition - Analysis and Applications

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Automated face recognition (AFR) aims to identify people in images or videos using pattern recognition techniques. Automated face recognition is widely used in applications ranging from social media to advanced authentication systems. Whilst techniques for face recognition are well established, the automatic recognition of faces captured by digital cameras in unconstrained, real-world environment is still very challenging, since it involves important variations in both acquisition conditions as well as in facial expressions and in pose changes. Thus, this chapter introduces the topic of computer automated face recognition in light of the main challenges in that research field and the developed solutions and applications based on image processing and artificial intelligence methods.

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Labeled Faces in the Wild: A Survey

Cited by 356 publications

References 92 publications

Development of a Computer System for Identity Authentication Using Artificial Neural Networks

Development of a Computer System for Identity Authentication Using Artificial Neural Networks

AgeDB: The First Manually Collected, In-the-Wild Age Database

Automated Face Recognition: Challenges and Solutions

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