na-xia Goto Github PK

advcam

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)

advcam-hide-adv-with-natural-styles

Code for "Adversarial Camouflage: Hiding Physical World Attacks with Natural Styles" (CVPR 2020)

adversarial-occlussion-aware-face-detection

adversarial_examples

对抗样本

computer-vision-action

computer vision learning, include python machine learning action; computer vision based on deep learning ;coursera deeplearning.ai and other cv learning materials collect ...

dast

A novel data-free model stealing method based on GAN

deep_learning_quick_reference

eigenface-recongnition

The summer course named Biometric in Depth in NUS

face-recognition-using-opencv-in-python

Face is most commonly used biometric to recognize people. Face recognition has received substantial attention from researchers due to human activities found in various applications of security like airport, criminal detection, face tracking, forensic etc. Compared to other biometric traits like palm print, Iris, finger print etc., face biometrics can be non-intrusive. They can be taken even without user’s knowledge and further can be used for security based applications like criminal detection, face tracking, airport security, and forensic surveillance systems. Face recognition involves capturing face image from a video or from a surveillance camera. They are compared with the stored database. Face biometrics involves training known images, classify them with known classes and then they are stored in the database. When a test image is given to the system it is classified and compared with stored database. Face biometrics is a challenging field of research with various limitations imposed for a machine face recognition like variations in head pose, change in illumination, facial expression, aging, occlusion due to accessories etc.,. Various approaches were suggested by researchers in overcoming the limitations stated. 72 Automatic face recognition involves face detection, feature extraction and face recognition. Face recognition algorithms are broadly classified into two classes as image template based and geometric feature based. The template based methods compute correlation between face and one or more model templates to find the face identity. Principal component analysis, linear discriminate analysis, kernel methods etc. are used to construct face templates. The geometric feature based methods are used to analyze explicit local features and their geometric relations (elastic bung graph method). Multi resolution tools such as contour lets, ridge lets were found to be useful for analyzing information content of images and found its application in image processing, pattern recognition, and computer vision. Curvelets transform is used for texture classification and image de-noising. Application of Curvelets transform for feature extraction in image processing is still under research.

face_attention_network

Pytorch implementation of face attention network

face_crop_align_mtcnn

Crop and Align Face by MTCNN

face_recognition_augmentation_with_partial_occlusion

face_recognition_occlusion

Face recognition implementation is capable of recognizing faces with occlusion, this includes faces wearing masks.

facenet

Face recognition using Tensorflow

insightface

Face Analysis Project on PyTorch and MXNet

insightface-tensorflow

Tensoflow implementation of InsightFace (ArcFace: Additive Angular Margin Loss for Deep Face Recognition).

keras-attention

Visualizing RNNs using the attention mechanism

keras-dcgan

Keras implementation of Deep Convolutional Generative Adversarial Networks

linux-3.10

《深入理解Linux网络（张彦飞）》； 《内核解密》；

masknet-occlusion-invariant-face-recognition-system

MaskNet is an Occluded face recognition system which capable in recognition masked faces. The project is developed through the research study. the research study can found here. https://www.researchgate.net/project/Face-Mask-Invariant-Face-Recognition-with-Identity-Verification

ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

naxia

occlusion-face-recognition

occlusion face recognition

pdsn

PDSN code for ICCV'2019 paper 《Occlusion Robust Face Recognition based on Mask Learning with Pairwise Differential Siamese Network》

python_vlpr

毕业设计基于Opencv的车牌识别系统

rude-carnie

Age detection in Tensorflow

smoofaceengine

基于深度学习的高性能人脸识别引擎Demo

symmfcnet

Learning Symmetry Consistent Deep CNNs for Face Completion

understand_facenet

整理和注释了facenet的代码，以便更好地理解facenet的代码。源代码地址：https://github.com/davidsandberg/facenet

yousan.ai

Awesome resources of yousan.ai(closely related to deep learning).