On the frontiers of pose invariant face recognition: a review

Ahmed, Sheikh Bilal; Ali, Syed Ahmad; Ahmad, Jameel; Adnan, Muhammad; Fraz, Muhammad Moazam

doi:10.1007/s10462-019-09742-3

Cited by 28 publications

(15 citation statements)

References 154 publications

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“…The authors in [12] tested the recognition system performance of a modelled age-invariant face recognition system after passing face images through the designed and optimized adaptive neuro-fuzzy inference system (ANFIS) classifier. The reviews made in [13] and [14] give in-depth studies of the performances of various augmented datasets on designed age-invariant face recognition system. The studies focused on the challenges of face recognition as it relates to the verification of designed face recognition systems using different augmented datasets.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of augmented datasets performances of age invariant face recognition models

et al. 2021

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The popularity of face recognition systems has increased due to their non-invasive method of image acquisition, thus boasting the widespread applications. Face ageing is one major factor that influences the performance of face recognition algorithms. In this study, the authors present a comparative study of the two most accepted and experimented face ageing datasets (FG-Net and morph II). These datasets were used to simulate age invariant face recognition (AIFR) models. Four types of noises were added to the two face ageing datasets at the preprocessing stage. The addition of noise at the preprocessing stage served as a data augmentation technique that increased the number of sample images available for deep convolutional neural network (DCNN) experimentation, improved the proposed AIFR model and the trait aging features extraction process. The proposed AIFR models are developed with the pre-trained Inception-ResNet-v2 deep convolutional neural network architecture. On testing and comparing the models, the results revealed that FG-Net is more efficient over Morph with an accuracy of 0.15%, loss function of 71%, mean square error (MSE) of 39% and mean absolute error (MAE) of -0.63%.

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Section: Introductionmentioning

confidence: 99%

Comparative analysis of augmented datasets performances of age invariant face recognition models

et al. 2021

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“…Even better results could be achieved with larger training datasets [22], better encoder architectures [115], data augmentation [116] and other methods [117], providing models with the robustness to large age [51] and pose [118] variations and ability to overcome the problems of racial bias [119], domain imbalance [54] and bad image quality [120].…”

Section: ) Megafacementioning

confidence: 99%

Prototype Memory for Large-Scale Face Representation Learning

Smirnov¹,

Garaev²,

Galyuk³

2022

IEEE Access

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Face representation learning using datasets with a massive number of identities requires appropriate training methods. Softmax-based approach, currently the state-of-the-art in face recognition, in its usual "full softmax" form is not suitable for datasets with millions of persons. Several methods, based on the "sampled softmax" approach, were proposed to remove this limitation. These methods, however, have a set of disadvantages. One of them is a problem of "prototype obsolescence": classifier weights (prototypes) of the rarely sampled classes, receive too scarce gradients and become outdated and detached from the current encoder state, resulting in incorrect training signals. This problem is especially serious in ultra-large-scale datasets. In this paper, we propose a novel face representation learning model called Prototype Memory, which alleviates this problem and allows training on a dataset of any size. Prototype Memory consists of the limited-size memory module for storing recent class prototypes and employs a set of algorithms to update it in appropriate way. New class prototypes are generated on the fly using exemplar embeddings in the current mini-batch. These prototypes are enqueued to the memory and used in a role of classifier weights for softmax classification-based training. To prevent obsolescence and keep the memory in close connection with the encoder, prototypes are regularly refreshed, and oldest ones are dequeued and disposed of. Prototype Memory is computationally efficient and independent of dataset size. It can be used with various loss functions, hard example mining algorithms and encoder architectures. We prove the effectiveness of the proposed model by extensive experiments on popular face recognition benchmarks.

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“…Machine learning approach can be broadly classified into supervised, unsupervised, and deep learning [? ], [34].…”

Section: Machine Learning Approachesmentioning

confidence: 99%

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

Ahmed

Ali²,

Khan

2021

IEEE Access

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On the frontiers of pose invariant face recognition: a review

Cited by 28 publications

References 154 publications

Comparative analysis of augmented datasets performances of age invariant face recognition models

Comparative analysis of augmented datasets performances of age invariant face recognition models

Prototype Memory for Large-Scale Face Representation Learning

On the Frontiers of Rice Grain Analysis, Classification and Quality Grading: A Review

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