Face illumination processing via dense feature maps and multiple receptive fields

[1]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[2]  Jiwen Lu,et al.  Single Sample Face Recognition via Learning Deep Supervised Autoencoders , 2015, IEEE Transactions on Information Forensics and Security.

[3]  Jian-Huang Lai,et al.  Face Image Illumination Processing Based on Generative Adversarial Nets , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[4]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[5]  Jian-Huang Lai,et al.  Face Image Illumination Processing Based on GAN with Dual Triplet Loss , 2018, PRCV.

[6]  M. J. Er,et al.  Illumination normalisation for face recognition in transformed domain , 2010 .

[7]  Alexei A. Efros,et al.  The Unreasonable Effectiveness of Deep Features as a Perceptual Metric , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Keren Fu,et al.  Illumination normalization of face image , 2020, International Conference on Digital Image Processing.

[10]  Li Fei-Fei,et al.  Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.

[11]  Tieniu Tan,et al.  A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.

[12]  Turgay Celik,et al.  FER‐Net: facial expression recognition using densely connected convolutional network , 2019, Electronics Letters.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Changhui Hu,et al.  IL-GAN: Illumination-invariant representation learning for single sample face recognition , 2019, J. Vis. Commun. Image Represent..

[15]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[16]  Alexei A. Efros,et al.  Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Keren Fu,et al.  A High-Performance Face Illumination Processing Method via Multi-Stage Feature Maps , 2020, Sensors.

[19]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[21]  Yanli Liu,et al.  Asymmetric Joint GANs for Normalizing Face Illumination From a Single Image , 2020, IEEE Transactions on Multimedia.

[22]  Keren Fu,et al.  An Identity-Preserved Model for Face Sketch-Photo Synthesis , 2020, IEEE Signal Processing Letters.

[23]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[24]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.