A Deep Learning-Based Detection of Wrinkles on Skin

Deepa, H.; Gowrishankar, S.; Veena, A.

doi:10.1007/978-981-16-9573-5_2

Cited by 3 publications

(1 citation statement)

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“…For wrinkles in the nasolabial region, the method achieved an accuracy of 98.9%, which is a significant improvement, but the effect of the direct detection of wrinkles on the entire face might be diminished. Deepa et al [18] used image processing and deep convolutional neural networks (CNNs) to detect wrinkles on the skin of a human face. The algorithm recognizes regions of interest (ROIs) containing skin wrinkles and facial features, achieving 96% wrinkle detection.…”

Section: Introductionmentioning

confidence: 99%

Facial Wrinkle Detection with Multiscale Spatial Feature Fusion Based on Image Enhancement and ASFF-SEUnet

Chen,

He,

Cai

2023

Electronics

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Wrinkles, crucial for age estimation and skin quality assessment, present challenges due to their uneven distribution, varying scale, and sensitivity to factors like lighting. To overcome these challenges, this study presents facial wrinkle detection with multiscale spatial feature fusion based on image enhancement and an adaptively spatial feature fusion squeeze-and-excitation Unet network (ASFF-SEUnet) model. Firstly, in order to improve wrinkle features and address the issue of uneven illumination in wrinkle images, an innovative image enhancement algorithm named Coiflet wavelet transform Donoho threshold and improved Retinex (CT-DIR) is proposed. Secondly, the ASFF-SEUnet model is designed to enhance the accuracy of full-face wrinkle detection across all age groups under the influence of lighting factors. It replaces the encoder part of the Unet network with EfficientNet, enabling the simultaneous adjustment of depth, width, and resolution for improved wrinkle feature extraction. The squeeze-and-excitation (SE) attention mechanism is introduced to grasp the correlation and importance among features, thereby enhancing the extraction of local wrinkle details. Finally, the adaptively spatial feature fusion (ASFF) module is incorporated to adaptively fuse multiscale features, capturing facial wrinkle information comprehensively. Experimentally, the method excels in detecting facial wrinkles amid complex backgrounds, robustly supporting facial skin quality diagnosis and age assessment.

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Section: Introductionmentioning

confidence: 99%