IARPA Janus Benchmark-B Face Dataset

Whitelam, Cameron; Taborsky, Emma; Blanton, Austin; Maze, Brianna; Adams, Jocelyn; Miller, Tim; Kalka, Nathan D.; Jain, Anil K.; Duncan, James A.; Allen, Kristen; Cheney, Jordan; Grother, Patrick J.

doi:10.1109/cvprw.2017.87

Cited by 360 publications

(247 citation statements)

References 12 publications

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“…In this section, we describe the training dataset, validation set, and implementation details. We also demonstrate the effectiveness of the proposed AFRN on the LFW [11], YTF [37], IJB-A [17] and IJB-B [36] datasets.…”

Section: Methodsmentioning

confidence: 99%

“…• We show that the proposed AFRN improves effectively the accuracy of both face verification and face identification. • To investigate the effectiveness of the AFRN, we present extensive experiments on the public available datasets such as LFW [11], YTF [37], Cross-Age LFW (CALFW), Cross-Pose LFW (CPLFW), Celebrities in Frontal-Profile in the Wild (CFP) [30], AgeDB [23], IARPA Janus Benchmark-A (IJB-A) [17], IARPA Janus Benchmark-B (IJB-B) [36], and IARPA Janus Benchmark-C (IJB-C) [22]. ded features.…”

Section: Introductionmentioning

confidence: 99%

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Attentional Feature-Pair Relation Networks for Accurate Face Recognition

Kang

Kim

Jun

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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Human face recognition is one of the most important research areas in biometrics. However, the robust face recognition under a drastic change of the facial pose, expression, and illumination is a big challenging problem for its practical application. Such variations make face recognition more difficult. In this paper, we propose a novel face recognition method, called Attentional Feature-pair Relation Network (AFRN), which represents the face by the relevant pairs of local appearance block features with their attention scores. The AFRN represents the face by all possible pairs of the 9×9 local appearance block features, the importance of each pair is considered by the attention map that is obtained from the low-rank bilinear pooling, and each pair is weighted by its corresponding attention score. To increase the accuracy, we select top-K pairs of local appearance block features as relevant facial information and drop the remaining irrelevant. The weighted top-K pairs are propagated to extract the joint feature-pair relation by using bilinear attention network. In experiments, we show the effectiveness of the proposed AFRN and achieve the outstanding performance in the 1:1 face verification and 1:N face identification tasks compared to existing stateof-the-art methods on the challenging LFW, YTF, CALFW, CPLFW, CFP, AgeDB, IJB-A, IJB-B, and IJB-C datasets.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Attentional Feature-Pair Relation Networks for Accurate Face Recognition

Kang

Kim

Jun

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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show abstract

“…It has tough variability with face imagery presenting a wide range of poses, harsh illuminations, occlusions, aging and other challenging conditions. For all these reasons, the series IJB-{A,B,C} [12,26,21] quickly became the de facto standard for face recognition in the wild.…”

Section: Ijbc-irdmentioning

confidence: 99%

AIRD: Adversarial Learning Framework for Image Repurposing Detection

Jaiswal¹,

Wu²,

AbdAlmageed³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Image repurposing is a commonly used method for spreading misinformation on social media and online forums, which involves publishing untampered images with modified metadata to create rumors and further propaganda. While manual verification is possible, given vast amounts of verified knowledge available on the internet, the increasing prevalence and ease of this form of semantic manipulation call for the development of robust automatic ways of assessing the semantic integrity of multimedia data. In this paper, we present a novel method for image repurposing detection that is based on the real-world adversarial interplay between a bad actor who repurposes images with counterfeit metadata and a watchdog who verifies the semantic consistency between images and their accompanying metadata, where both players have access to a reference dataset of verified content, which they can use to achieve their goals. The proposed method exhibits state-of-the-art performance on location-identity, subject-identity and painting-artist verification, showing its efficacy across a diverse set of scenarios.

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“…Probes have one-to-many faces. Following template conventions of other many-to-many face evaluations, facial images for unique subjects are separated by identity, with a gallery containing variable number of relatives, each with a variable number of faces [32]. Teams were allowed to submit up to six final submissions, with each submissions being a ranked-list of all subjects in the gallery.…”

Section: Fmsmentioning

confidence: 99%

Recognizing Families In the Wild (RFIW)

Robinson

Shao

Zhao

et al. 2017

Proceedings of the 2017 Workshop on Recognizing Families in the Wild

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Recognizing Families In the Wild (RFIW)-an annual large-scale, multi-track automatic kinship recognition challenge supporting visual kin-based problems on scales larger than before. Organized in conjunction with the 15 th IEEE International Conference on Automatic Face and Gesture Recognition (FG), RFIW provides a platform for publishing original work and the gathering of experts for a discussion of the next steps. This paper summarizes the supported tasks (i.e., kinship verification, tri-subject verification, and search & retrieval of missing children) in the evaluation protocols, which include the practical motivation, technical background, data splits, metrics, and benchmark results. Furthermore, top submissions (i.e., leader-board stats) are listed and reviewed as a high-level analysis on the state of the problem. In the end, the purpose of this paper is to describe the 2020 RFIW challenge, end-to-end, along with forecasts in promising future directions.

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IARPA Janus Benchmark-B Face Dataset

Cited by 360 publications

References 12 publications

Attentional Feature-Pair Relation Networks for Accurate Face Recognition

Attentional Feature-Pair Relation Networks for Accurate Face Recognition

AIRD: Adversarial Learning Framework for Image Repurposing Detection

Recognizing Families In the Wild (RFIW)

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