Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy...Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy and visual fidelity.This paper introduces a new deep generative framework,Modified Multi-Scale Fused CycleGAN(MMF-CycleGAN),for robust and photo-realistic profile-to-frontal face synthesis.The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder.The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions,leading to sharper textures and improved global facial geometry.Also,GAN training stability and identity preservation are improved through the Ranger optimizer,which effectively balances adversarial,identity,and cycle-consistency losses.Experiments on three benchmark datasets show that MMFCycleGAN achieves accuracy of 0.9541,0.9455,and 0.9422,F1-scores of 0.9654,0.9641,and 0.9614,and AUC values of 0.9742,0.9714,and 0.9698,respectively,and the extreme-pose accuracy(yaw>60°)reaches 0.92.Despite its enhanced architecture,the framework maintains an efficient inference time of 0.042 s per image,making it suitable for real-time biometric authentication,surveillance,and security applications in unconstrained environments.展开更多
Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual...Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.展开更多
文摘Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes,self-occlusions,and the complete loss of important structural and textural components,depressing recognition accuracy and visual fidelity.This paper introduces a new deep generative framework,Modified Multi-Scale Fused CycleGAN(MMF-CycleGAN),for robust and photo-realistic profile-to-frontal face synthesis.The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder.The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions,leading to sharper textures and improved global facial geometry.Also,GAN training stability and identity preservation are improved through the Ranger optimizer,which effectively balances adversarial,identity,and cycle-consistency losses.Experiments on three benchmark datasets show that MMFCycleGAN achieves accuracy of 0.9541,0.9455,and 0.9422,F1-scores of 0.9654,0.9641,and 0.9614,and AUC values of 0.9742,0.9714,and 0.9698,respectively,and the extreme-pose accuracy(yaw>60°)reaches 0.92.Despite its enhanced architecture,the framework maintains an efficient inference time of 0.042 s per image,making it suitable for real-time biometric authentication,surveillance,and security applications in unconstrained environments.
文摘Image super-resolution reconstruction technology is currently widely used in medical imaging,video surveillance,and industrial quality inspection.It not only enhances image quality but also improves details and visual perception,significantly increasing the utility of low-resolution images.In this study,an improved image superresolution reconstruction model based on Generative Adversarial Networks(SRGAN)was proposed.This model introduced a channel and spatial attention mechanism(CSAB)in the generator,allowing it to effectively leverage the information from the input image to enhance feature representations and capture important details.The discriminator was designed with an improved PatchGAN architecture,which more accurately captured local details and texture information of the image.With these enhanced generator and discriminator architectures and an optimized loss function design,this method demonstrated superior performance in image quality assessment metrics.Experimental results showed that this model outperforms traditional methods,presenting more detailed and realistic image details in the visual effects.
