State-of-the-art face recognition systems are based on deep (convolutional) neural networks. Therefore, it is imperative to determine to what extent face templates derived from deep networks can be inverted to obtain the original face image. In this paper, we discuss the vulnerabilities of a face recognition system based on deep templates, extracted by deep networks under image reconstruction a...

State-of-the-art 3D morphable model (3DMM) is used widely for 3D face reconstruction based on a single image. However, this method has a high computational cost, and hence, a simplified 3D morphable model (S3DMM) was proposed as an alternative. Unlike the original 3DMM, S3DMM uses only a sparse 3D facial shape, and therefore, it incurs a lower computational cost. However, this method is vulnera...

Handling pose variations for face recognition system is a challenging task. The recognition rate is drastically decreasing with the images captured in uncontrolled environment having pose variations in yaw, pitch and roll angles. When the face image with frontal pose it is proved that the recognition system performs well. In this research an attempt is made to reconstruct frontal pose face imag...

This paper proposes an analysis-by-synthesis framework for face recognition with variant pose, illumination and expression. First, an efficient 2D-to3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination; Then, realistic virtual faces with different of pose, illuminatio...

Uncovering the neural dynamics of facial identity processing along with its representational basis outlines a major endeavor in the study of visual processing. To this end, here, we record human electroencephalography (EEG) data associated with viewing face stimuli; then, we exploit spatiotemporal EEG information to determine the neural correlates of facial identity representations and to recon...

In this paper, we propose a novel face feature extraction method based on deep learning. Using synthesized multi-view face images, we train a deep face feature (DFF) extractor based on the correlation between projections of a face point on images from different views. A feature vector can be extracted for each pixel of the face image based on the trained DFF model, and it is more effective than...

Radical parotidectomy often results in complex facial nerve defects involving the main nerve trunk and multiple distal nerve branches. Although cable nerve grafting often leads to good nerve regeneration, severe synkinesis due to aberrant axonal regrowth is inevitable. In such situations, the use of 2 motor sources to differentially reanimate the upper and lower face could minimize synkinesis. ...

This paper presents a 3D face reconstruction method using multiple 2D face images. Structure from motion (SfM) methods, which have been widely used to reconstruct 3D faces, are vulnerable to point correspondence errors caused by self-occlusion. In order to solve this problem, we propose a shape conversion matrix (SCM) which estimates the ground-truth 2D facial feature points (FFPs) from the obs...

In this paper, we propose an elegant approach for illumination invariant face recognition based on the photometric stereo technique. The basic idea is to reconstruct the surface normal and the albedo of a face using photometric stereo images, and then use them as the illumination independent model of the face. And, we have investigated the optimal light source directions for accurate surface sh...

Face reenactment and reconstruction benefit various applications in self-media, VR, etc. Recent face methods use 2D facial landmarks to implicitly retarget expressions poses from driving videos source images, while they suffer pose expression preservation issues for cross-identity scenarios, i.e., when the subjects are different. Current self-supervised also demonstrate impressive results. Howe...