Pinned Repositories
Coupled-GAN
Developed a coupled generative adversarial network (CpGAN) architecture for cross-spectral iris recognition by projecting the visible and near-infrared iris images into a low-dimensional embedding domain to explore the hidden relationship between them.
CrossModal_GAN
We design a cross-modal GAN which learns image-to-image modality transformation across cross-domain. This network is able to synthesize Infrared images from VISIBLE images for VEDAI dataset
CrossSpectral-CrossResolution-cGAN
A cGAN architecture is designed to simultaneously perform both cross-spectral synthesis and super-resolution from a low-resolution (LR) NIR iris to a synthesized high-resolution (HR) visible iris.
Face-Frontal-GAN
Implement a Generative Adversarial Network which is able to frontalize faces from the pose invariant features learned in our proposed pose attention guided profile to frontal network for extreme profile faces
Face-Recognition-at-Extreme-Pitch-and-Yaw-View-Angles
Implemented pose aware face recognition network to Improve the performance of face recognition task for the faces at extreme joint pitch and yaw view angles.
Joint-Super-Resolution-and-Vehicle-Detection-Network
Implemented a novel deep joint network to perform both super-resolution and detection simultaneously using multi-scale GAN framework which attains superior results against the state-of-the-art methods.
Pose-Attention-Guided-Profile-to-Frontal-Face-Recognition
Developed a deep novel coupled profile to frontal face recognition network incorporating pose as an auxiliary information via attention mechanism (i.e., implemented a pose attention module).
tatamoktari.github.io
Brief Bio about me
TataMoktari's Repositories
TataMoktari/Pose-Attention-Guided-Profile-to-Frontal-Face-Recognition
Developed a deep novel coupled profile to frontal face recognition network incorporating pose as an auxiliary information via attention mechanism (i.e., implemented a pose attention module).
TataMoktari/Coupled-GAN
Developed a coupled generative adversarial network (CpGAN) architecture for cross-spectral iris recognition by projecting the visible and near-infrared iris images into a low-dimensional embedding domain to explore the hidden relationship between them.
TataMoktari/CrossModal_GAN
We design a cross-modal GAN which learns image-to-image modality transformation across cross-domain. This network is able to synthesize Infrared images from VISIBLE images for VEDAI dataset
TataMoktari/CrossSpectral-CrossResolution-cGAN
A cGAN architecture is designed to simultaneously perform both cross-spectral synthesis and super-resolution from a low-resolution (LR) NIR iris to a synthesized high-resolution (HR) visible iris.
TataMoktari/Face-Frontal-GAN
Implement a Generative Adversarial Network which is able to frontalize faces from the pose invariant features learned in our proposed pose attention guided profile to frontal network for extreme profile faces
TataMoktari/Face-Recognition-at-Extreme-Pitch-and-Yaw-View-Angles
Implemented pose aware face recognition network to Improve the performance of face recognition task for the faces at extreme joint pitch and yaw view angles.
TataMoktari/Joint-Super-Resolution-and-Vehicle-Detection-Network
Implemented a novel deep joint network to perform both super-resolution and detection simultaneously using multi-scale GAN framework which attains superior results against the state-of-the-art methods.
TataMoktari/tatamoktari.github.io
Brief Bio about me