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

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.