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Name: Will
Type: User
Company: CSU
Bio: Will.Zhang,software school,Centural South University
Location: Hunan,ChangSha,China
Name: Will
Type: User
Company: CSU
Bio: Will.Zhang,software school,Centural South University
Location: Hunan,ChangSha,China
The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.
微信定时推送早安
基于python3.5、django1.10、xadmin的多用户博客论坛系统
我的大数据学习书单
eBook分享大集合:主要以IT领域经典书籍收藏,以备不时之需。
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.
人脸表情识别
Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).
A collection of useful .gitignore templates
《实战突击:Java Web项目整合开发》源码
教程→ http://t.cn/zQ6LFwE 赞助→ http://t.cn/R5bhVpf 推送请使用UTF-8编码
This project is to get top100 on Maoyan
A real-time facial expression recognition system with webcam streaming and CNN
This project is to tell you how to create VPN
Config files for my GitHub profile.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
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JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
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Some thing interesting about visualization, use data art
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We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
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Data-Driven Documents codes.
China tencent open source team.