Pinned Repositories
AI-papers
人工智能相关论文
-Micro-expression-recognition-based-on-spatiotemporal-features
Micro-expression recognition based on spatiotemporal features
AFARtoolbox
AFAR: A Deep Learning Based Toolbox for Automated Facial Affect Recognition
AKMNet-Micro-Expression
Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining
AlphaTree-graphic-deep-neural-network
机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN），图神经网络（GNN），NLP，大数据相关的发展路书(roadmap), 并附海量源码（python，pytorch）带大家消化基本知识点，突破面试，完成从新手到合格工程师的跨越，其中深度学习相关论文附有tensorflow caffe官方源码，应用部分含推荐算法和知识图谱
hexoblog
我的hexo个人博客源码，基于hexo-butterfly主题模板修改
JavaLearn
后端开发知识管理，思维导图
MyProject
人工智能、深度学习、JAVA、python、研究生阶段的学习资料以及项目代码分享
SMIC_MER_VGGFace2-LSTM
Using VGGFace2 and Long short-term memory network to do the Micro-expression Recognition Task by implementing SMIC datasets.
VM-SMIC-MER-VGGface2-LSTM
Micro-expression Recognition based on the video magnification and CNN+RNN structures
William9527wn's Repositories
William9527wn/SMIC_MER_VGGFace2-LSTM
Using VGGFace2 and Long short-term memory network to do the Micro-expression Recognition Task by implementing SMIC datasets.
William9527wn/VM-SMIC-MER-VGGface2-LSTM
Micro-expression Recognition based on the video magnification and CNN+RNN structures
William9527wn/MyProject
人工智能、深度学习、JAVA、python、研究生阶段的学习资料以及项目代码分享
William9527wn/hexoblog
我的hexo个人博客源码，基于hexo-butterfly主题模板修改
William9527wn/AFARtoolbox
AFAR: A Deep Learning Based Toolbox for Automated Facial Affect Recognition
William9527wn/AKMNet-Micro-Expression
Recognizing Micro-Expression in Video Clip with Adaptive Key-Frame Mining
William9527wn/JavaLearn
后端开发知识管理，思维导图
William9527wn/AWESOME-FER
🔆 Top conferences & Journals focused on Facial expression recognition (FER)/ Facial action unit (FAU) 💫 ✨
William9527wn/awesome-java
Collection of awesome Java project on Github(非常棒的 Java 开源项目集合).
William9527wn/Blog-1
vue + springboot 前后端分离博客
William9527wn/blog_admin
博客后台管理系统前端项目
William9527wn/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++
William9527wn/dataease
人人可用的开源数据可视化分析工具。
William9527wn/Emotion-Detection-in-Videos
The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.
William9527wn/Emotion-FAN
ICIP 2019: Frame Attention Networks for Facial Expression Recognition in Videos
William9527wn/expert_readed_books
2021年最新总结，推荐工程师合适读本，计算机科学，软件技术，创业，**类，数学类，人物传记书籍
William9527wn/Facial-Expression-Recognition.Pytorch
A CNN based pytorch implementation on facial expression recognition (FER2013 and CK+), achieving 73.112% (state-of-the-art) in FER2013 and 94.64% in CK+ dataset
William9527wn/geek_blog
一个基于SpringBoot+Mybatis(MybatisPlus)+SpringSecurity+Redis+ElasticSearch的个人博客系统，预览地址：
William9527wn/micro_expression_pytorch
Using LSTM and CNN on CASME2 dataset
William9527wn/MicroExpression
William9527wn/OpenFace
OpenFace – a state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.
William9527wn/PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
William9527wn/PyTorch-Networks
Pytorch implementation of cnn network
William9527wn/seckill
William9527wn/Self-Cure-Network
This is a novel and easy method for annotation uncertainties.
William9527wn/stacked-capsule-networks
Pytorch Implementation of the Stacked Capsule Autoencoders
William9527wn/vit-pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
William9527wn/William9527wn
William9527wn/wnblog
基于Springboot的个人博客系统
William9527wn/wnblogImg
我的blog图床