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On the frontiers of pose invariant face recognition: a review

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Abstract

Computer vision systems open a new challenge to recognize human faces under varied poses in similar capacity and capability as human-beings perform naturally. For surveillance applications, pose-invariant face recognition (PIFR) will become a major break-through by presenting the solution of this unique challenge. In recent decade, several techniques are presented to address this challenge over well-known data-sets. These efforts are divided chronologically into seven different approaches say geometric, statistical, holistic, template, supervised learning, unsupervised learning and deep learning. Among these deep learning techniques have shown more promising results and have gained attention for future research. By reviewing PIFR, it is historically divided into five eras based on 160 referred papers and their cumulative citations.

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Abbreviations

AAM:

Active appearance models

ASM:

Active shape model

BU3DFE:

Binghamton University 3D facial expression

CMUPIE:

Carnegie Mellon University-pose illumination expression

CNN:

Convolutional neural network

CLS:

Correspondence latent subspace

CSGPR:

Coupled Scale Gaussian Process Regression

DCCA:

Deep canonical correlation analysis

DNN:

Deep neural network

DR-GAN:

Disentagled representation learning-generative adversarial

FR:

Face recognition

FERET:

Face recognition technology

FSS:

Face-specific subspace

FLD:

Fisher linear discriminant

GTP:

Gabor ternary pattern

GMM:

Gaussian mixture model

GMA:

Generalized multi-view analysis

GEM:

Generic elastic model

HMM:

Hidden Markov models

HLA:

Hybrid learning algorithm

ICP:

Iterative closest point

KL:

Karmen Loeve

KCCA:

Kernel canonical correlation analysis

LFW:

Labeled faces in the wild

LBP:

Linear binary pattern

LDA:

Linear discriminant analysis

MRF:

Markov random field

MKD:

Multi key descriptor

M2VTS:

Multi modal verification for teleservices and security application

MultiPIE:

Multi-pose illumination expression

PaSC:

Point and shoot challenge face recognition

PIFR:

Pose invariant face recognition

PCA:

Principal component analysis

PEM:

Probabilistic elastic model

PEP:

Probabilistic elastic part

PubFig:

Public figures face database

RBF:

Radial basis function

RGT:

Re-normalization group theory

RBM:

Restricted Boltzmann machine

SIFT:

Scale invariant feature transform

SJP:

Single jet presentation

SVD:

Singular value decomposition

SRC:

Sparse representation classification

YTC:

YouTube faces database

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Authors and Affiliations

  1. Department of Software Engineering, SST, University of Management and Technology, Lahore, Pakistan

    Sheikh Bilal Ahmed & Syed Farooq Ali

  2. Department of Electrical Engineering, SEN, University of Management and Technology, Lahore, Pakistan

    Jameel Ahmad & Muhammad Adnan

  3. The Alan Turing Institute, British Library, NW1 2DB, London, UK

    Muhammad Moazam Fraz

  4. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Muhammad Moazam Fraz

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  1. Sheikh Bilal Ahmed
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Ahmed, S.B., Ali, S.F., Ahmad, J. et al. On the frontiers of pose invariant face recognition: a review. Artif Intell Rev 53, 2571–2634 (2020). https://doi.org/10.1007/s10462-019-09742-3

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