Proceedings ArticleDOI
Pose-Aware Face Recognition in the Wild
Iacopo Masi,Stephen Rawls,Gerard Medioni,Prem Natarajan +3 more
- pp 4838-4846
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TLDR
A method to push the frontiers of unconstrained face recognition in the wild by using multiple pose specific models and rendered face images called Pose-Aware Models (PAMs), which achieve remarkably better performance than commercial products and surprisingly also outperform methods that are specifically fine-tuned on the target dataset.Abstract:
We propose a method to push the frontiers of unconstrained face recognition in the wild, focusing on the problem of extreme pose variations. As opposed to current techniques which either expect a single model to learn pose invariance through massive amounts of training data, or which normalize images to a single frontal pose, our method explicitly tackles pose variation by using multiple posespecific models and rendered face images. We leverage deep Convolutional Neural Networks (CNNs) to learn discriminative representations we call Pose-Aware Models (PAMs) using 500K images from the CASIA WebFace dataset. We present a comparative evaluation on the new IARPA Janus Benchmark A (IJB-A) and PIPA datasets. On these datasets PAMs achieve remarkably better performance than commercial products and surprisingly also outperform methods that are specifically fine-tuned on the target dataset.read more
Citations
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Proceedings ArticleDOI
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
Luan Tran,Xi Yin,Xiaoming Liu +2 more
TL;DR: Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.
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L2 constrained softmax loss for discriminative face verification
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Journal ArticleDOI
Face Alignment in Full Pose Range: A 3D Total Solution
TL;DR: Wang et al. as mentioned in this paper proposed a 3D Dense Face Alignment (3DDFA) framework, in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks.
Proceedings ArticleDOI
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
TL;DR: A new loss function, namely Wing loss, for robust facial landmark localisation with Convolutional Neural Networks (CNNs) is presented, and the superiority of the proposed method over the state-of-the-art approaches is proved.
References
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