Enhancing Kinship Verification through Multiscale Retinex and Combined Deep-Shallow features

• URL: http://arxiv.org/abs/2312.03562v1
• Date: Wed, 6 Dec 2023 15:52:31 GMT
• Title: Enhancing Kinship Verification through Multiscale Retinex and Combined Deep-Shallow features
• Authors: El Ouanas Belabbaci, Mohammed Khammari, Ammar Chouchane, Mohcene Bessaoudi, Abdelmalik Ouamane, Yassine Himeur, Shadi Atalla and Wathiq Mansoor
• Abstract summary: This study integrates a preprocessing method named Multiscale Retinex (MSR), which amplifies image quality and elevates contrast. For deep feature extraction, we turn to the prowess of the VGG16 model, which is pre-trained on a convolutional neural network. The robustness and efficacy of our method have been put to the test through meticulous experiments on three rigorous kinship datasets.
• Score: 2.829220962949294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The challenge of kinship verification from facial images represents a cutting-edge and formidable frontier in the realms of pattern recognition and computer vision. This area of study holds a myriad of potential applications, spanning from image annotation and forensic analysis to social media research. Our research stands out by integrating a preprocessing method named Multiscale Retinex (MSR), which elevates image quality and amplifies contrast, ultimately bolstering the end results. Strategically, our methodology capitalizes on the harmonious blend of deep and shallow texture descriptors, merging them proficiently at the score level through the Logistic Regression (LR) method. To elucidate, we employ the Local Phase Quantization (LPQ) descriptor to extract shallow texture characteristics. For deep feature extraction, we turn to the prowess of the VGG16 model, which is pre-trained on a convolutional neural network (CNN). The robustness and efficacy of our method have been put to the test through meticulous experiments on three rigorous kinship datasets, namely: Cornell Kin Face, UB Kin Face, and TS Kin Face.

Related papers

• Improving Neural Surface Reconstruction with Feature Priors from Multi-View Image [87.00660347447494]
  Recent advancements in Neural Surface Reconstruction (NSR) have significantly improved multi-view reconstruction when coupled with volume rendering. We propose an investigation into feature-level consistent loss, aiming to harness valuable feature priors from diverse pretext visual tasks. Our results, analyzed on DTU and EPFL, reveal that feature priors from image matching and multi-view stereo datasets outperform other pretext tasks.
  arXiv  Detail & Related papers  (2024-08-04T16:09:46Z)
• Towards More General Video-based Deepfake Detection through Facial Feature Guided Adaptation for Foundation Model [15.61920157541529]
  We propose a novel Deepfake detection approach by adapting the Foundation Models with rich information encoded inside. Inspired by the recent advances of parameter efficient fine-tuning, we propose a novel side-network-based decoder. Our approach exhibits superior effectiveness in identifying unseen Deepfake samples, achieving notable performance improvement.
  arXiv  Detail & Related papers  (2024-04-08T14:58:52Z)
• Multi-Scale Texture Loss for CT denoising with GANs [0.9349653765341301]
  Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. This work presents a loss function that leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence Matrix (GLCM) Our approach also introduces a self-attention layer that dynamically aggregates the multi-scale texture information extracted from the images.
  arXiv  Detail & Related papers  (2024-03-25T11:28:52Z)
• Fusion of Deep and Shallow Features for Face Kinship Verification [0.44855664250147465]
  This work makes notable contributions by incorporating a preprocessing technique known as Multiscale Retinex (MSR) Our approach harnesses the strength of complementary deep (VGG16) and shallow texture descriptors (BSIF) by combining them at the score level using Logistic Regression (LR) technique.
  arXiv  Detail & Related papers  (2023-12-16T14:36:43Z)
• Neural Point-based Volumetric Avatar: Surface-guided Neural Points for Efficient and Photorealistic Volumetric Head Avatar [62.87222308616711]
  We propose fullname (name), a method that adopts the neural point representation and the neural volume rendering process. Specifically, the neural points are strategically constrained around the surface of the target expression via a high-resolution UV displacement map. By design, our name is better equipped to handle topologically changing regions and thin structures while also ensuring accurate expression control when animating avatars.
  arXiv  Detail & Related papers  (2023-07-11T03:40:10Z)
• Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection [24.098604827919203]
  We propose a joint learning strategy with deep texture and high-frequency features for CG image detection. A semantic segmentation map is generated to guide the affine transformation operation. The combination of the original image and the high-frequency components of the original and rendered images are fed into a multi-branch neural network equipped with attention mechanisms.
  arXiv  Detail & Related papers  (2022-09-07T17:30:40Z)
• Rank-Enhanced Low-Dimensional Convolution Set for Hyperspectral Image Denoising [50.039949798156826]
  This paper tackles the challenging problem of hyperspectral (HS) image denoising. We propose rank-enhanced low-dimensional convolution set (Re-ConvSet) We then incorporate Re-ConvSet into the widely-used U-Net architecture to construct an HS image denoising method.
  arXiv  Detail & Related papers  (2022-07-09T13:35:12Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition [59.29808528182607]
  Cross-resolution face recognition (CRFR) is important in intelligent surveillance and biometric forensics. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR.
  arXiv  Detail & Related papers  (2021-03-25T14:03:42Z)
• DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring [66.91879314310842]
  We propose an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale cascaded feature refinement module then predicts the deblurred image from the deconvolved deep features. We show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts and quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin.
  arXiv  Detail & Related papers  (2021-03-18T00:38:11Z)
• Kernelized dense layers for facial expression recognition [10.98068123467568]
  We propose a Kernelized Dense Layer (KDL) which captures higher order feature interactions instead of conventional linear relations. We show that our model achieves competitive results with respect to the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-09-22T21:02:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.