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c3d

classification-images

Using a multi-task CNN to classify images of faces

coloring

Auto-coloring for gray images

convert-image-to-csv

Takes an image which contains a spectra and put it's values to csv file so that you could open it with Origin or other program which analyzes graphics

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.

emotionrecognition

Emotion recognition using keras

extract_image_data

extract_image_data (Images2CSV) - Fast multithreaded script for converting a large number of images to CSV data

face_align

人脸图片对齐，让眼睛固定在输出图片的固定位置。

facedetection

Each face is 60x70 and from the Berkeley classification project which took the photos from the Cal-tech 101 data set. There are 451 training images, 150 test images, and 301 Validation images. Perceptron, Naive Bayes, and KNN are used for classification.

facenet

Face recognition using Tensorflow

facenet_distance

用FaceNet模型计算两个人脸之间的距离

facial-expression-recognition-svm

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

facialexpressionrecognition

Deep learning for the facial expression recognition kaggle challenge

find_similar_images

In this project, I have used OpenCV library in Python to find the similar images in a dataset. The methods that have been used are Color Histograms and Feature Matching to predict whether given two images are similar or not.

hyperface-tensorflow-implementation

HyperFace

imagestovideo

Script for using FFMPEG to convert a series of timestamped images to video

imgaug

Image augmentation for machine learning experiments.

inc_v3_fer

Facial Expression Recognition using Inception V3 Model in keras

keras-transfer-learning-for-oxford102

Keras pretrained models (VGG16, InceptionV3, Resnet50, Resnet152) + Transfer Learning for predicting classes in the Oxford 102 flower dataset

keras_finetunevggface_savemodel_fitgenerator

keras_tests_cnn

Keras CNN: InceptionNet, VGG16, ResNet

learningtensorflow

mtcnn-tensorflow

Reproduce MTCNN using Tensorflow

multi-task-learning

Multi-task learning smile detection, age and gender classification on GENKI4k, IMDB-Wiki dataset.

my-tensorflow-tutorials

This repo contains all of my TensorFlow tutorials

mystore

my research on deep learning

nn-c3d

C3D features + neural networks

owl-vit-object-detection

object detection based on owl-vit

personlabeling

skillsdevelope

to develope my skills