Pinned Repositories
awesome-human-pose-estimation
A collection of awesome resources in Human Pose estimation.
C-
CNN-Network-Architectures
CS129-18-face-detection
:chicken: An face-detection platform based on Gabor filters, with classifications done via ANN and Naive Bayes.
Deep-Learning-21-Examples
《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码
deep-learning-models
Keras code and weights files for popular deep learning models.
dlib
A toolkit for making real world machine learning and data analysis applications in C++
EmoPy
A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
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.
emotion-recognition-example
Emotion recognition example using the Jaffe database
electronicliujiang's Repositories
electronicliujiang/Deep-Learning-21-Examples
《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码
electronicliujiang/awesome-human-pose-estimation
A collection of awesome resources in Human Pose estimation.
electronicliujiang/C-
electronicliujiang/CNN-Network-Architectures
electronicliujiang/dlib
A toolkit for making real world machine learning and data analysis applications in C++
electronicliujiang/EmoPy
A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)
electronicliujiang/emotion-recognition-example
Emotion recognition example using the Jaffe database
electronicliujiang/Face-Recognizaiton-CNN-SIFT
Using SIFT to find keypoint, CNN to extract feature and classify ck and jaffe image
electronicliujiang/face_classification
Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.
electronicliujiang/face_recognition
The world's simplest facial recognition api for Python and the command line
electronicliujiang/face_recognition_py
基于OpenCV的视频人脸识别
electronicliujiang/Facial-Expression-Detection
Facial Expression or Facial Emotion Detector can be used to know whether a person is sad, happy, angry and so on only through his/her face. This Repository can be used to carry out such a task.
electronicliujiang/Facial-Expression-Recognition
Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).
electronicliujiang/facial-expression-recognition-svm
Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset
electronicliujiang/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
electronicliujiang/gitskills
electronicliujiang/gitskills1
electronicliujiang/HOGNeuralNet
Creation of a neural network to classify HOG data gathered from the Jaffe database.
electronicliujiang/ipad
ipad_connect
electronicliujiang/learn_go
learning go/ practicing go
electronicliujiang/Learnbot-Emotion-Recognition-Proposed-Model
Landmarks - Masks - Cascades (Hog - Gabor - LBP) - Action Units - Xgboost
electronicliujiang/LEARNGIT
electronicliujiang/learngit1
electronicliujiang/learning_git
electronicliujiang/Lihang
Statistical learning methods, 统计学习方法 [李航] 值得反复读. [笔记, 代码, notebook, 参考文献, Errata]
electronicliujiang/Machine-Learning
:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归
electronicliujiang/my-vim
electronicliujiang/NN-MNIST-
基于两层神经网络的手写数字识别
electronicliujiang/RAVEN
electronicliujiang/torch-mesh-isect