Pose-Aware Face Recognition in the Wild

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 image...

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Bibliographic Details
Published in2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) pp. 4838 - 4846
Main Authors Masi, Iacopo, Rawls, Stephen, Medioni, Gerard, Natarajan, Prem
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Benchmark testing
Face
Face recognition
Rendering (computer graphics)
Solid modeling
Three-dimensional displays
Training
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Summary: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 pose specific 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.
ISSN:1063-6919
DOI:10.1109/CVPR.2016.523