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PosIX-GAN: Generating multiple poses using GAN for pose-invariant face recognition
Avishek Bhattacharjee, Samik Banerjee,
Published in Springer Verlag
2019
Volume: 11131 LNCS
   
Pages: 427 - 443
Abstract
Pose-Invariant Face Recognition (PIFR) has been a serious challenge in the general field of face recognition (FR). The performance of face recognition algorithms deteriorate due to various degradations such as pose, illuminaton, occlusions, blur, noise, aliasing, etc. In this paper, we deal with the problem of 3D pose variation of a face. for that we design and propose PosIX Generative Adversarial Network (PosIX-GAN) that has been trained to generate a set of nice (high quality) face images with 9 different pose variations, when provided with a face image in any arbitrary pose as input. The discriminator of the GAN has also been trained to perform the task of face recognition along with the job of discriminating between real and generated (fake) images. Results when evaluated using two benchmark datasets, reveal the superior performance of PosIX-GAN over state-of-the-art shallow as well as deep learning methods. © Springer Nature Switzerland AG 2019.
About the journal
JournalData powered by TypesetLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherData powered by TypesetSpringer Verlag
ISSN03029743
Open AccessNo
Concepts (13)
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    Benchmarking
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    Computer vision
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    Deep learning
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    Gesture recognition
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    ADVERSARIAL NETWORKS
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    Benchmark datasets
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    FACE RECOGNITION ALGORITHMS
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    Learning methods
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    Multitask learning
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    POSE
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    POSE-INVARIANT FACE RECOGNITION
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    State of the art
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    Face recognition