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.

Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset

Comparison of classification algorithms for the task of Facial Expression Recognition. Also includes real-time Facial Expression Recognition via screen capture (Dlib 18.18, OpenCV 2.4.11/3.1, SVM+MLP+AdaBoost+NBayes).

This nootebook ilustrate the using Histogram of oriented gradients descriptor features with RBF based SVM for facial expression recognition.

Contrast multiple facial expression recognition experiments and found that using SVM instead of softmax layer can achieve better classification results(65.47% accuracy on fer2013 dataset).

Linear Regression, Ridge Regression, SVM, Facial Expression Recognition

Facial Expression Recognition using SVM and TensorFlow. Detecting faces in video and recognize the expression(emotion).

Nhận diện biểu cảm khuôn mặt sử dụng SVM

Working code for human facial expression(happy, sad, surprize, fear, angry) recognition using Support Vector Machine (SVM ML algorithm))

Facial expression recognition using SVM with DLIB

the project of facial expression recognition using SVM

It's a B.Sc. final year report on Facial Expression Recognition using SVM and CNN

Facial expression recognition. This project includes 3 algorithms: least square, perceptron Algorithm, SVM. programming code is matlab and c++.

Engagement Recognition Using Video blends AWS SageMaker, Python, TensorFlow, OpenCV, and SVM to detect boredom, engagement, confusion, and frustration from facial expressions in video calls. Combining CNN and facial landmarks, it builds smarter meetings by decoding attention in real time.

Automated facial expression recognition systems have many applications. With the success of deep learning techniques FER systems are getting better. This project presents a comprehensive survey on deep facial expression recognition systems . A comparative study is carried out using various convolutional neural networks and support vector machine models. It is shown that combining the power of CNN and SVM gives a better performance.

This machine learning project aimed at assessing public service officers' friendliness in real-time. Using models like MobileNet and SVM, it detects emotions such as happiness or anger from facial expressions. With datasets like KDEF and RAF-DB, this tool offers an efficient way to evaluate service quality through facial recognition.

A project to compare different approaches in Automatic Facial Expression Recognition, usinga SVM, Neural Network and other classifiers.

FER

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Facial Expression Recognition Using Facial Landmarks And SVMs

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No description available

Facial Expressions Recognition using Mediapipe and SVM

Facial expression recognition done using SVM and CNN,

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Facial Expression Recognition with VGG16 Feature Extraction and Traditional Classifiers (SVM vs Random Forest)

Facial Expression Recognition using CNN, SVM, and Random Forest

Facial Expression Recognition with SVM, KNN and VGG-16

Recognition of Face and Facial Expressions using PCA, SVM and CNN

Gender and facial Expressions Recognition: using HOG, HOF, SVM and openCV