As an important public place in Colleges and universities, how to use its resources efficiently is of vital importance. It is of great practical significance to make full use of library resources by using library surveillance video and counting the number of people in various areas of the library. This topic focuses on the method of human detection in Library video, and uses the corresponding classifier to detect and count the target.
python实现GBDT的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，庖丁解牛地理解GBDT。Gradient Boosting Decision Trees regression, dichotomy and multi-classification are realized based on python, and the details of algorithm flow are displayed, interpreted and visualized to help readers better understand Gradient Boosting Decision
implement of "One millisecond face alignment with an ensemble of regression tree" in python
The project uses state of the art deep learning on collected data for automatic analysis of emotions.
Facial Expression Recognition (Fer2013) using aggregated CNN/VGG models with SIFT/DSIFT descriptors
Implement a VGG-like network capable of predicting emotion and facial expressions
deep learning for image processing including classification and object-detection etc.
Joint Pose and Expression Modeling for Facial Expression Recognition
Training SVM classifier to recognize people expressions (emotions) on Fer2013 dataset
Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream
Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks
Experimentation of deep learning on the subjects of micro-expression spotting and recognition.
Facial Expression Recognition Using Eigenface Method in MATLAB
Classify each facial image into one of the seven facial emotion categories considered using CNN based on https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge
