Unsupervised face recognition in the wild using high-dimensional features under super-resolution and 3D alignment effect

El-Sayed, Ahmed; Kongar, Elif; Mahmood, Ausif; Sobh, Tarek M.

doi:10.1007/s11760-018-1289-6

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…In addition, SR methods based on deep learning have also made significant progress in recent years [ 15 , 16 ]. However, the current research on the role of the SR algorithm in recognition is focused on specific fields such as 2D face recognition [ 17 , 18 ]. To the best of our knowledge, no relevant study covers the role of the SR algorithm in range image recognition.…”

Section: Introductionmentioning

confidence: 99%

Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution Algorithm

Zhai

Jin

Xia

et al. 2020

Sensors

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This work introduces a super-resolution (SR) algorithm for range images on the basis of self-guided joint filtering (SGJF), adding the range information of the range image as a coefficient of the filter to reduce the influence of the intensity image texture on the super-resolved image. A range image SR recognition system is constructed to study the effect of four SR algorithms including the SGJF algorithm on the recognition of the laser radar (ladar) range image. The effects of different model library sizes, SR algorithms, SR factors and noise conditions on the recognition are tested via experiments. Results demonstrate that all tested SR algorithms can improve the recognition rate of low-resolution (low-res) range images to varying degrees and the proposed SGJF algorithm has a very good comprehensive recognition performance. Finally, suggestions for the use of SR algorithms in actual scene recognition are proposed on the basis of the experimental results.

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Section: Introductionmentioning

confidence: 99%

Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution Algorithm

Zhai

Jin

Xia

et al. 2020

Sensors

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Attention-enhanced multi-scale residual network for single image super-resolution

Sun

Qin

Gao

et al. 2022

SIViP

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Comparative Analysis of Supervised and Unsupervised Approaches Applied to Large-Scale “In The Wild” Face Verification

Hachaj¹,

Mazurek²

2020

Symmetry

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Deep learning-based feature extraction methods and transfer learning have become common approaches in the field of pattern recognition. Deep convolutional neural networks trained using tripled-based loss functions allow for the generation of face embeddings, which can be directly applied to face verification and clustering. Knowledge about the ground truth of face identities might improve the effectiveness of the final classification algorithm; however, it is also possible to use ground truth clusters previously discovered using an unsupervised approach. The aim of this paper is to evaluate the potential improvement of classification results of state-of-the-art supervised classification methods trained with and without ground truth knowledge. In this study, we use two sufficiently large data sets containing more than 200,000 “taken in the wild” images, each with various resolutions, visual quality, and face poses which, in our opinion, guarantee the statistical significance of the results. We examine several clustering and supervised pattern recognition algorithms and find that knowledge about the ground truth has a very small influence on the Fowlkes–Mallows score (FMS) of the classification algorithm. In the case of the classification algorithm that obtained the highest accuracy in our experiment, the FMS improved by only 5.3% (from 0.749 to 0.791) in the first data set and by 6.6% (from 0.652 to 0.718) in the second data set. Our results show that, beside highly secure systems in which face verification is a key component, face identities discovered by unsupervised approaches can be safely used for training supervised classifiers. We also found that the Silhouette Coefficient (SC) of unsupervised clustering is positively correlated with the Adjusted Rand Index, V-measure score, and Fowlkes–Mallows score and, so, we can use the SC as an indicator of clustering performance when the ground truth of face identities is not known. All of these conclusions are important findings for large-scale face verification problems. The reason for this is the fact that skipping the verification of people’s identities before supervised training saves a lot of time and resources.

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Unsupervised face recognition in the wild using high-dimensional features under super-resolution and 3D alignment effect

Cited by 3 publications

References 23 publications

Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution Algorithm

Research on the Enhancement of Laser Radar Range Image Recognition Using a Super-Resolution Algorithm

Attention-enhanced multi-scale residual network for single image super-resolution

Comparative Analysis of Supervised and Unsupervised Approaches Applied to Large-Scale “In The Wild” Face Verification

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