Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

A powerful baseline for image classification, face recognition and image retrieval with Pytorch

Practise of DeepID for Face Classification

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.

Face Detection and Gender and Age Classification using Keras

Face model to classify gender and race. Trained on LFWA+ Dataset.

[CVPR2022] End-to-End Reconstruction-Classification Learning for Face Forgery Detection

Deep Learning workshop including image classification, face recognition, Object detection, language modelling, image captioning and neural machine translation.

Official code for fast face classification

Computer Vision project that detects emotion, age and gender after detecting faces.

real-time face detection and emotion classification

Two image models for face expression recognition and for ethnic classification

This repository presents a couple of approaches to the problem of multi-view image classification. I faced this challenge during a hackathon in which I participated, and decided to share my code here. I've also written a Medium article to provide further details and explanations. Feel free to check it out !

💥 Use Hugging Face text and token classification pipelines directly in spaCy

User friendly zero-shot image classification using open-source models from the Hugging Face library

Labelled face dataset for face shape classification

Implementation of Protein Classification based on subcellular localization using ProtBert(Rostlab/prot_bert_bfd_localization) model from Hugging Face library, based on BERT model trained on large corpus of protein sequences.

Building a Neural Network that classifies faces using OpenCV and Tensorflow

Face recognition using dlib and kNN classification (ROS compatible)

Face classification on JavaScript

Use Cafffe to do Face Attributes MultiTask Classification based on CelebA data sets

No description available

This repository is related to a project of the Introduction to Numerical Imaging (i.e, Introduction à l'Imagerie Numérique in French), given by the MVA Masters program at ENS-Paris Saclay. It was entirely build from scratch and contains code in PyTorch Lightning to train and then use a neural network for image classification. We used it to create a classifier allowing semantic attributes classification of faces with the dataset CelebA-HQ.

Project aimed at presenting a model to find a vacant parking spot in real time and ensure car safety using Deep Learning (Parking spot Classification and Face recognition).

This project classifies female facial images into 5 different face shapes using Convolutional Neural Networks.

Modular real-time computer vision system | YOLOv8 object detection · face detection · motion analysis · edge extraction · multi-object tracking · CNN classification | OpenCV + PyTorch

DEmoClassi stands for Demographic (age, gender, race) and Emotions (happy, sad, angry, ...) Classification from face images, using deep learning.

Provide personalized color recommendation using face detection, segmentation and image classification model

Delve into practical computer vision and image processing projects and get up to speed with advanced object detection techniques and machine learning algorithms Key Features Discover best practices for engineering and maintaining OpenCV projects Explore important deep learning tools for image classification Understand basic image matrix formats and filters Book Description OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch

face recognition via Sparse Representation-based Classification (SRC)