hamzamohdzubair Goto Github PK

awesome-alternatives-in-rust

A curated list of replacements for existing software written in Rust

awesome-math

A curated list of awesome mathematics resources

clm-framework

CLM-framework (a.k.a Cambridge Face Tracker) is a framework for various Constrained Local Model based face tracking and landmark detection algorithms and their extensions/applications. Includes CLM-Z and CLNF.

convolutionalemotion

A deep convolutional neural network system for live emotion detection

courses-nlp

courses-nlp-101

datasciencecoursera

datasharing

The Leek group guide to data sharing

dlib

A toolkit for making real world machine learning and data analysis applications in C++

emotion

Motion visualtion using simple camera

emotion-detection

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

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

A program to detect faces from live feed of webcam and correctly identify the emotion of the person from just the face.

emotion-recognition-dof

Emotion classifier that predicts emotion based on facial movements using Dense Optical Flow

emotion-recognition-rnn

Recurrent Neural Networks for Emotion Recognition in Video

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).

extract_face_features

A simple ROS package to extract facial landmarks

face-expression-detect

face-expression prediction using python

facemoji

Detects face and its emotion, draws according emoji over face

facial-geometric-features-extraction

facial-landmark-annotation-tools

A python GUI implementation for faster annotation with keyboard shortcuts.

flex

The minimalist Pelican theme.

getting-and-cleaning-data-final-assignment

ggplot2-book

The ggplot2 book

goldpelican

graphene-gae

GraphQL Support for Google AppEngine [DEPRECATED - Looking for maintainers]

hamzamohdzubai-humco1

hamzamohdzubair.github.io

head-tracking

This project can perform head tracking and change persepective of viewing 3D objects in Python using OpenCV

hitchhikers_ggplot2