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Learning Face Representation from Scratch
TLDR
This paper proposes a semi-automatical way to collect face images from Internet and builds a large scale dataset containing about 10,000 subjects and 500,000 images, called CASIAWebFace. Expand
Face Alignment Across Large Poses: A 3D Solution
TLDR
This paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). Expand
Single-Shot Refinement Neural Network for Object Detection
TLDR
We propose a novel single-shot based detector, called RefineDet, that achieves better accuracy than two-stage methods and maintains comparable efficiency of one- stage methods. Expand
A face antispoofing database with diverse attacks
TLDR
Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. Expand
S^3FD: Single Shot Scale-Invariant Face Detector
TLDR
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S3FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Expand
Deep Metric Learning for Person Re-identification
TLDR
This paper proposed a deep metric learning method by using siamese convolutional neural network. Expand
Learning Multi-scale Block Local Binary Patterns for Face Recognition
TLDR
We propose a novel representation, calledMultiscale Block Local Binary Pattern (MB-LBP), to overcome the limitations of LBP, and apply it to face recognition. Expand
High-fidelity Pose and Expression Normalization for face recognition in the wild
TLDR
We propose a High-fidelity Pose and Expression Normalization method with 3D Morphable Model (3DMM) which can automatically generate a natural face image in frontal pose and neutral expression. Expand
The CASIA NIR-VIS 2.0 Face Database
TLDR
In recent years, heterogeneous face biometrics has attracted more attentions in the face recognition community. Expand
Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection
TLDR
We propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. Expand
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