fitrialif Goto Github PK

eeg_controlled_rover

A BCI implementation to control a rover with your thoughts.

eeg_convolutionsl_neural_net

A convolutional neural network programmed in python using the Keras machine learning framework used to categorize brain signal based on what a user was looking at when the EEG data was collected.

eeg_emotion

EEG sentiment analysis

effectivetensorflow

TensorFlow tutorials and best practices.

elephas

Distributed Deep learning with Keras & Spark

emnist

A project designed to explore CNN and the effectiveness of RCNN on classifying the EMNIST dataset.

emonet

emonet-pytorch

PyTorch Implementation of EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Network (Abdul-Mageed et al. 2017)

emopy

A deep neural net toolkit for emotion analysis via Facial Expression Recognition (FER)

emopy-capsnet

emotime

Recognizing emotional states in faces

emotion

:smile: Recognizes human faces and their corresponding emotions from a video or webcam feed. Powered by OpenCV and Deep Learning.

emotion-1

emotion classifier

emotion-and-gender-classification

2 networks to recognition gender and emotion; face detection using Opencv or Mtcnn

emotion-classification-comparison

Classification comparison between various models and learning on emotion datasets

emotion-classifier

Facial emotion classifier using Keras

emotion-conv-net

Real-time emotion recognition using convolutional neural nets.

emotion-detection

Look into a webcam and the program will tell you whether you are smiling or not.

emotion-detection-1

Facial emotion detection with TFLearn

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-detection-pytorch

Detect emotions from faces using transfer learning in pytorch

emotion-detection-with-keras

Training ML model using Tensorflow and Keras

emotion-recognition

Emotion recognition from face

emotion-recognition-1

Facial emotion recognition using TensorFlow

emotion-recognition-2

Use tensorflow and machine learning algorithms to predict the emotion depicted by an image of a face.

emotion-recognition-3

This python module will recognize emotions in video sequences using optical flow and machine learning techniques.

emotion-recognition-example

Emotion recognition example using the Jaffe database

emotion-recognition-rnn

Recurrent Neural Networks for Emotion Recognition in Video

emotion-recognition-using-python

The python code detects different landmarks on the face and predicts the emotions such as smile based on it. It automatically takes a photo of that person when he smiles. Also when the two eyebrows are lifted up, the system plays a music automatically and the music stops when you blink your right eye.

emotion-recognition-using-svms

A software which detect a human face through live webcam feed and identifies the emotion of the person (i.e. the person is happy or sad).