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amhocr

Tesseract Powered Windows Desktop OCR Application With Multiple Pre/Post Processing GUI

awesome-computer-vision

A curated list of awesome computer vision resources

awesome-deep-learning-papers

The most cited deep learning papers

awesome-deep-vision

A curated list of deep learning resources for computer vision

awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

caffe-face

This branch is developed for deep face recognition

calamari

Line based ATR Engine based on OCRopy

chineseocr_lite

超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , psenet(8.5M) + crnn(6.3M) + anglenet(1.5M) 总模型仅17M

datapublic

deconvfaces

Generating faces with deconvolution networks

deep-residual-networks

Deep Residual Learning for Image Recognition

deepalignmentnetwork

A deep neural network for face alignment

deepframeworks

Evaluation of Deep Learning Frameworks

deeplearningbook

MIT Deep Learning Book in PDF format

deeplearningbook-chinese

Deep Learning Book Chinese Translation

deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

denoisingautoencoder

Python implementation of Stacked Denoising Autoencoders for unsupervised learning of high level feature representation

dlib

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

ee368

EE368 Project: Facial Expression Recognition under Variable Lighting

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.

emotiw2016

emotiw2017

everydaysport

从今天开始做一个健康的程序员。

face-landmarks-detection-benchmark

Face landmarks(fiducial points) detection benchmark

face_recognition

The world's simplest facial recognition api for Python and the command line

facenet

Face recognition using Tensorflow

facial-expression-classifiers

facial-expression-recognition

Facial expression recognition deep learning examples

facial-expression-recognition-svm

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

facialexpressionrecognition

Fer2013 Facial Expression Recognition Keras