Face Recognition: From Traditional to Deep Learning Methods
arXiv:1811.00116
Abstract
Starting in the seventies, face recognition has become one of the most researched topics in computer vision and biometrics. Traditional methods based on hand-crafted features and traditional machine learning techniques have recently been superseded by deep neural networks trained with very large datasets. In this paper we provide a comprehensive and up-to-date literature review of popular face recognition methods including both traditional (geometry-based, holistic, feature-based and hybrid methods) and deep learning methods.
References in corpus (13)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- Learning Face Representation from Scratch
- DeepID3: Face Recognition with Very Deep Neural Networks
- Invertible Conditional GANs for image editing
- Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
- Representation Learning by Rotating Your Faces
- ExprGAN: Facial Expression Editing with Controllable Expression Intensity
- Emergence of Complex-Like Cells in a Temporal Product Network with Local Receptive Fields
- Range Loss for Deep Face Recognition with Long-tail
- Semi-Latent GAN: Learning to generate and modify facial images from attributes
- Deep Pyramidal Residual Networks with Separated Stochastic Depth
- DeepVisage: Making face recognition simple yet with powerful generalization skills
Cited by in corpus (5)
- PrivacyNet: Semi-Adversarial Networks for Multi-attribute Face Privacy
- A survey of face recognition techniques under occlusion
- Classic versus deep learning approaches to address computer vision challenges
- Drone LAMS: A Drone-based Face Detection Dataset with Large Angles and Many Scenarios
- Deep learning for identification and face, gender, expression recognition under constraints