Frontal to profile face verification in the wild

Sengupta, Shamik; Chen, Jun-Cheng; Castillo, Carlos D.; Patel, Vishal M.; Chellappa, Rama; Jacobs, David W.

doi:10.1109/wacv.2016.7477558

Cited by 676 publications

(427 citation statements)

References 35 publications

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“…We perform experiments with different noise rates, supervised AM-Losses and computing weighted loss methods(see Section 4.2.2) with the experiment in the Section 3.2. The models are evaluated on Labelled Faces in the Wild (LFW) [16], Celebrities in Frontal Profile (CFP) [37], and Age Database (AgeDB) [28]. As shown in Table 1, competitive performance can be achieved using our paradigm, without any prior knowledge about noise in training data.…”

Section: Methodsmentioning

confidence: 99%

Noise-Tolerant Paradigm for Training Face Recognition CNNs

Huang²,

Zhang

et al. 2019

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

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Benefit from large-scale training datasets, deep Convolutional Neural Networks(CNNs) have achieved impressive results in face recognition(FR). However, tremendous scale of datasets inevitably lead to noisy data, which obviously reduce the performance of the trained CNN models. Kicking out wrong labels from large-scale FR datasets is still very expensive, although some cleaning approaches are proposed. According to the analysis of the whole process of training CNN models supervised by angular margin based loss(AM-Loss) functions, we find that the θ distribution of training samples implicitly reflects their probability of being clean. Thus, we propose a novel training paradigm that employs the idea of weighting samples based on the above probability. Without any prior knowledge of noise, we can train high performance CNN models with largescale FR datasets. Experiments demonstrate the effectiveness of our training paradigm. The codes are available at https://github.com/huangyangyu/NoiseFace.

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Section: Methodsmentioning

confidence: 99%

Noise-Tolerant Paradigm for Training Face Recognition CNNs

Huang²,

Zhang

et al. 2019

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

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“…This information set could be named from the Superstars in Front-Profile (CFP) data-set. We analyzed the recital greater than several different calculations employing a restricted process and described how those all embarrass by Front-Front to Front-Account [33].…”

Section: Literature Review Of Associated Workmentioning

confidence: 99%

Unconstrained Face Recognition Quality: A Review

Tyagi¹,

Tomar²,

Shrivastava³

2016

IJSIP

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“…• 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%

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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Frontal to profile face verification in the wild

Cited by 676 publications

References 35 publications

Noise-Tolerant Paradigm for Training Face Recognition CNNs

Noise-Tolerant Paradigm for Training Face Recognition CNNs

Unconstrained Face Recognition Quality: A Review

Attentional Feature-Pair Relation Networks for Accurate Face Recognition

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