Towards nonuniform illumination face enhancement via adaptive contrast stretching

Mustapha, Aouache; Oulefki, Adel; Bengherabi, Messaoud; Boutellaa, Elhocine; Algaet, Mustafa Almahdi

doi:10.1007/s11042-017-4665-2

Cited by 10 publications

(3 citation statements)

References 33 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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“…Linear contrast stretching (LCS) is an IET that uses linear transformation to increase the dynamic range of gray levels present in an original image [18]. LCS improves the contrast grade of an image; however, the LCS's threshold value must be manually configured.…”

Section: Introductionmentioning

confidence: 99%

A new evaluation function for face image enhancement in unconstrained environments using metaheuristic algorithms

Oloyede

Hancke

Myburgh

et al. 2019

J Image Video Proc.

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Image enhancement is an integral component of face recognition systems and other image processing tasks such as in medical and satellite imaging. Among a number of existing image enhancement methods, metaheuristic-based approaches have gained popularity owing to their highly effective performance rates. However, the need for improved evaluation functions is a major research concern in the study of metaheuristic-based image enhancement methods. Thus, in this paper, we present a new evaluation function for improving the performance of metaheuristic-based image enhancement methods. Essentially, we applied our new evaluation function in conjunction with metaheuristic-based optimization algorithms in order to select automatically the best enhanced face image based on a linear combination of different key quantitative measures. Furthermore, different from other existing evaluation functions, our evaluation function is finitely bounded to determine easily whether an image is either too dark or too bright. This makes it better suited to find optimal solutions (best enhanced images) during the search process. Our method was compared with existing metaheuristic-based methods and other state-of-the-art image enhancement techniques. Based on the qualitative and quantitative measures obtained, our approach is shown to enhance facial images in unconstrained environments significantly.

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