Chloe GUO's starred repositories
bird-descriptor
Code for BMVC 2019 paper, BIRD: Learning Binary and Illumination Robust Descriptor for Face Recognition
me_recognition
CapsuleNet for Micro-expression Recognition (IEEE FG 2019)
Deception-Detection
Detecting Deception using Verbal Cues | Dataset Used: Real life trial data collected during a series of experiments at Michigan (http://web.eecs.umich.edu/~zmohamed/PDFs/Trial.ICMI.pdf) and Deceptive Opinion Spam Corpus v1.4(https://myleott.com/op-spam.html)
Deception-Detection-on-Amazon-reviews-dataset
A SVM model that classifies the reviews as real or fake. Used both the review text and the additional features contained in the data set to build a model that predicted with over 85% accuracy without using any deep learning techniques.
deep_motion_mag
Tensorflow implementation of Learning-based Video Motion Magnification
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.
Face-Spoofing-Detection
Deep Texture feature extraction and implementing Local Binary Pattern(LBP)-based Convolutional Neural Network
micro_expression_res3d_CASME2_tensorflow
A res_3D based detection system for micro expression recognition
Micro-Expression-with-Deep-Learning
Experimentation of deep learning on the subjects of micro-expression spotting and recognition.
lip-reading-deeplearning
:unlock: Lip Reading - Cross Audio-Visual Recognition using 3D Architectures
MotionMagnification
Eulerian, Lagrangian, and Phase-Based Motion Magnification in MATLAB
ActiveShapeModels
Face detection using active shape models 🐍
Dlib_face_detection_from_camera
Real-time facial landmarks detection / 摄像头人脸检测并进行特征点标定
Angular-Triplet-Loss
ATL(Angular Triplet Loss):A new loss function based on sphereface & triplet loss
person-reid-triplet-loss-baseline
Rank-1 89% (Single Query) on Market1501 with raw triplet loss, In Defense of the Triplet Loss for Person Re-Identification, using Pytorch
pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
chest_xray_14
Benchmarks on NIH Chest X-ray 14 dataset
Label-embeddings-in-image-classification
Convolutional Neural Networks (CNNs) are being widely used for various tasks in Computer Vision. We focus on the task of image classification particularly using CNNs with more focus on the relation or similarity between class labels. The similarity between labels is judged using label word embeddings and incorporated into the loss layer. We propose that shallower networks be learnt with more complex and structured losses, in order to gain from shorter training time and equivalent complexity. We train two variants of CNNs with multiple architectures , all limited to a maximum of ten convolution layers to obtain an accuracy of 93.27% on the Fashion-MNIST dataset and 86.40% on the CIFAR 10 dataset. We further probe the adversarial robustness of the model as well the classspecific behavior by visualizing the class confusion matrix.We also show some preliminary results towards extending a trained variant to zero-shot learning.
tensorboardX
tensorboard for pytorch (and chainer, mxnet, numpy, ...)