Learn More
Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. We notice that currently most of face anti-spoofing databases focus on data with little variations, which may limit the generalization performance of trained models since potential attacks in real world are probably more complex. In this paper we(More)
The goal of face hallucination is to generate high-resolution images with fidelity from low-resolution ones. In contrast to existing methods based on patch similarity or holistic constraints in the image space, we propose to exploit local image structures for face hallucination. Each face image is represented in terms of facial components, contours and(More)
This paper formulates face labeling as a conditional random field with unary and pairwise classifiers. We develop a novel multi-objective learning method that optimizes a single unified deep convolutional network with two distinct non-structured loss functions: one encoding the unary label likelihoods and the other encoding the pairwise label dependencies.(More)
We present a novel framework for hallucinating faces of un-constrained poses and with very low resolution (face size as small as 5pxIOD 1). In contrast to existing studies that mostly ignore or assume pre-aligned face spatial configuration (e.g. facial landmarks localization or dense correspondence field), we alternatingly optimize two complementary tasks,(More)
Heterogeneous Face Recognition (HFR) refers recognition of face images captured in different modalities, e.g. Visual (VIS), near infrared (NIR) and thermal infrared (TIR). Although heterogeneous face images of a given person differ by pixel values, the identity of the face should be classified as the same. This paper focuses on NIR-VIS HFR. Light Source(More)
In this paper, we consider numerous low-level vision problems (e.g., edge-preserving filtering and denoising) as recursive image filtering via a hybrid neural network. The network contains several spatially variant recurrent neural networks (RNN) as equivalents of a group of distinct recursive filters for each pixel, and a deep convolutional neu-ral network(More)
This paper presents an occlusion robust image representation method and apply it to face recognition. Inspired from the recent work [15], we propose a Gabor phase difference representation for occlusion robust face recognition. Based on the good ability of Gabor filters to capture image structure and the robustness to image occlusion shown in this paper,(More)
In this paper, we propose an algorithm to hallucinate faces in the JPEG compressed domain, which has not been well addressed in the literature. The proposed approach hallucinates compressed face images through an exemplar-based framework and solves two main problems. First, image noise introduced by JPEG compression is exacerbated through the(More)