Fuzzy reasoning model to improve face illumination invariance

Oulefki, Adel; Mustapha, Aouache; Boutellaa, Elhocine; Bengherabi, Messaoud; Tifarine, Ahmed Amine

doi:10.1007/s11760-017-1174-8

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…In the future work, we are planning to combine our weaklysupervised learning with semi-supervised learning to detect landmark of jawline. And we also hope to add some enactment and filtering algorithms [5]- [7] into face preprocessing stage to enhance face. Moreover, DCGAN and LR-CNN models could be shared features and reformed to an end-toend model to accelerate training and testing.…”

Section: Discussionmentioning

confidence: 99%

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Facial Component-Landmark Detection With Weakly-Supervised LR-CNN

Zhang

et al. 2019

IEEE Access

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In this paper, we propose a weakly supervised landmark-region-based convolutional neural network (LR-CNN) framework to detect facial component and landmark simultaneously. Most of the existing course-to-fine facial detectors fail to detect landmark accurately without lots of fully labeled data, which are costly to obtain. We can handle the task with a small amount of finely labeled data. First, deep convolutional generative adversarial networks are utilized to generate training samples with weak labels, as data preparation. Then, through weakly supervised learning, our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data. Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks. Detecting unblocked components first helps us to focus on the informative area, resulting in a better performance. Additionally, to improve the performance of the above tasks, we design two models as follows: 1) we add AnchorAlign in the region proposal networks to accurately localize components and 2) we propose a two-branch model consisting classification branch and regression branch to detect landmark. Extensive evaluations on benchmark datasets indicate that our proposed approach is able to complete the multi-task facial detection and outperforms the state-of-the-art facial component and landmark detection algorithms. INDEX TERMS Weakly-supervised, facial landmark, generative adversarial network, region-based convolutional neural network.

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Section: Discussionmentioning

confidence: 99%

“…Facial component and landmark detection are important procedures in a multitude of face analysis tasks including face recognition [1], [2], facial expression analysis [3], face reconstruction [4], and face enhancement [5]- [7]. With the enormous advancement of deep learning, the performance of many computer vision tasks, e.g.…”

Section: Introductionmentioning

confidence: 99%

Facial Component-Landmark Detection With Weakly-Supervised LR-CNN

Zhang

et al. 2019

IEEE Access

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“…This approach maps N observations or feature Mellon University". Other research, In ref [71] and ref [72], have incorporated fuzzy linear regression discriminant projection models in a similar direction. This type of machine learning analyses and clusters unlabeled datasets using machine learning algorithms, often recognized as unsupervised machine learning.…”

Section: Rough Set Theory Is a Statistical Technique Used In Multi-attribute Decision Analysis To Analyze Uncertainty And Vaguenessmentioning

confidence: 99%

A Survey on Classical and Modern Face Recognition Techniques

ShalimaSulthana

Nagaraju

2021

IJSRCSEIT

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During the previous few centuries, facial recognition systems have become a popular research topic. On account of its extraordinary success and vast social applications; it has attracted significant study attention from a wide range of disciplines in the last five years - including “computer-vision”, “artificial-intelligence”, and “machine-learning”. As with most face recognition systems, the fundamental goal involves recognizing a person's identity by means of images, video, data streams, and context information. As a result of our research; we've outlined some of the most important applications, difficulties, and trends in scientific and social domains. This research, the primary goal is to summarize modern facial recognition algorithms and to gain a general perceptive of how these techniques act on diverse datasets. Aside from that, we also explore some significant problems like illumination variation, position, aging, occlusion, cosmetics, scale, and background are some of the primary challenges we examine. In addition to traditional face recognition approaches, the most recent research topics such as sparse models, deep learning, and fuzzy set theory are examined in depth. There's also a quick discussion of basic techniques, as well as a more in-depth. As a final point, this research explores the future of facial recognition technologies and their possible importance in the emerging digital society.

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