End-to-End Facial Alignment and Recognition

Slides:

Advertisements

Similar presentations

Face Recognition: A Convolutional Neural Network Approach

Advertisements

Spatial Pyramid Pooling in Deep Convolutional

SOMTIME: AN ARTIFICIAL NEURAL NETWORK FOR TOPOLOGICAL AND TEMPORAL CORRELATION FOR SPATIOTEMPORAL PATTERN LEARNING.

Face Recognition Using Neural Networks Presented By: Hadis Mohseni Leila Taghavi Atefeh Mirsafian.

Convolutional Neural Networks for Image Processing with Applications in Mobile Robotics By, Sruthi Moola.

Rotation Invariant Neural-Network Based Face Detection

Detection, Segmentation and Fine-grained Localization

CAMEO: Face Recognition Year 1 Progress and Year 2 Goals Fernando de la Torre, Carlos Vallespi, Takeo Kanade.

Convolutional Neural Network

Spatial Localization and Detection

Deep Residual Learning for Image Recognition

Lecture 4b Data augmentation for CNN training

When deep learning meets object detection: Introduction to two technologies: SSD and YOLO Wenchi Ma.

Recent developments in object detection

Deep Residual Learning for Image Recognition

Hybrid Deep Learning for Reflectance Confocal Microscopy Skin Images

Faster R-CNN – Concepts

Object Detection based on Segment Masks

Data Mining, Neural Network and Genetic Programming

Computer Science and Engineering, Seoul National University

Convolutional Neural Fabrics by Shreyas Saxena, Jakob Verbeek

Perceptual Loss Deep Feature Interpolation for Image Content Changes

Robust Lung Nodule Classification using 2

Ajita Rattani and Reza Derakhshani,

Inception and Residual Architecture in Deep Convolutional Networks

Hierarchical Deep Convolutional Neural Network

Lecture 5 Smaller Network: CNN

CS6890 Deep Learning Weizhen Cai

Machine Learning: The Connectionist

Deep Residual Learning for Image Recognition

R-CNN region By Ilia Iofedov 11/11/2018 BGU, DNN course 2016.

Dynamic Routing Using Inter Capsule Routing Protocol Between Capsules

Low Dose CT Image Denoising Using WGAN and Perceptual Loss

By: Kevin Yu Ph.D. in Computer Engineering

Introduction to Neural Networks

Image Classification.

A Comparative Study of Convolutional Neural Network Models with Rosenblatt’s Brain Model Abu Kamruzzaman, Atik Khatri , Milind Ikke, Damiano Mastrandrea,

Wei Liu, Chaofeng Chen and Kwan-Yee K. Wong

Two-Stream Convolutional Networks for Action Recognition in Videos

Zhengjun Pan and Hamid Bolouri Department of Computer Science

Object Detection + Deep Learning

Creating Data Representations

A Proposal Defense On Deep Residual Network For Face Recognition Presented By SAGAR MISHRA MECE

Faster R-CNN By Anthony Martinez.

Age and Gender Classification using Convolutional Neural Networks

Lecture: Deep Convolutional Neural Networks

Spatial Transformer Networks

Visualizing CNNs and Deeper Deep Architectures

Use 3D Convolutional Neural Network to Inspect Solder Ball Defects

RCNN, Fast-RCNN, Faster-RCNN

Problems with CNNs and recent innovations 2/13/19

Neural Network Pipeline CONTACT & ACKNOWLEDGEMENTS

Inception-v4, Inception-ResNet and the Impact of

Convolutional Neural Network

Face Recognition: A Convolutional Neural Network Approach

Course Recap and What’s Next?

Abnormally Detection

Deep Learning Authors: Yann LeCun, Yoshua Bengio, Geoffrey Hinton

Human-object interaction

Visual Manipulation Relationship Network for Autonomous Robotics

Deep Object Co-Segmentation

Natalie Lang Tomer Malach

CS295: Modern Systems: Application Case Study Neural Network Accelerator Sang-Woo Jun Spring 2019 Many slides adapted from Hyoukjun Kwon‘s Gatech “Designing.

VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION

Neural Machine Translation using CNN

Object Detection Implementations

Learning Deconvolution Network for Semantic Segmentation

Deep screen image crop and enhance

Deep screen image crop and enhance

Presentation transcript:

End-to-End Facial Alignment and Recognition

Introduction Increase in face recognition accuracy

End-to-End Facial Alignment and Recognition

Why do we need STN Ideal Images Real Images

SPN predicts the coefficients of an affine transformation STN Architecture SPN predicts the coefficients of an affine transformation

Trying different localization architectures

SPN predicts the coefficients of an affine transformation STN Architecture SPN predicts the coefficients of an affine transformation

Parameterized Sampling grid The grid generator’s job is to output a parametrised sampling grid, which is a set of points where the input map should be sampled to produce the desired transformed output. The column vector xin, yin consists in a set of indices that tell us where we should sample our input to obtain the desired transformed output. Compute the pixel value in output image ,take the value in the input image at the right place

Spatial Transformer Network

Identity transformation

SPN predicts the coefficients of an affine transformation STN Architecture SPN predicts the coefficients of an affine transformation

Bilinear Interpolation

Differential Gradient

STN Result

STN Result

Recognition ResNet with 9 residual blocks 24 convolution layers in total 512 dimensional output feature vector

Results

End-to-End Spatial Transform Face Detection and Recognition

Architecture Region feature transformation Align the detected faces

Detection Similar to Faster R-CNN VGG-16 (pre-trained on Image-Net) Region Proposal Network ROI Pooling Spatial Transformer Network

Faster RCNN

Region Proposal

ROI Pooling

Architecture Region feature transformation Align the detected faces

SPN predicts the coefficients of an affine transformation STN Architecture SPN predicts the coefficients of an affine transformation

Recognition Another STN is added before the recognition part ResNet: ResNet with 9 residual blocks 24 convolution layers in total 512 dimensional output feature vector

Results

References Jaderberg, Max, Karen Simonyan, and Andrew Zisserman. "Spatial transformer networks." Advances in Neural Information Processing Systems. 2015. Chi, Liying, Hongxin Zhang, and Mingxiu Chen. "End-To-End Face Detection and Recognition." arXiv preprint arXiv:1703.10818 (2017).