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tongsong91's Projects

deepfuturegaze_gan icon deepfuturegaze_gan

Deep Future Gaze: Gaze Anticipation on Egocentric Videos Using Adversarial Networks

deepimagereconstruction icon deepimagereconstruction

Data and demo codes for Shen, Horikawa, Majima, and Kamitani, "Deep image reconstruction from human brain activity".

densenet icon densenet

Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).

densitymapping icon densitymapping

Python tools for creating density maps of events (drunk tweets, police shootings, etc)

developer icon developer

developer roadmap. use growth https://github.com/phodal/growth replace this

dlib-align-faces icon dlib-align-faces

A face detection tool based on Dlib C++ Library that can detect frontal faces, export the bounding box of the face chips as JSON with based64 embedded jpeg images

extremelearningmachine icon extremelearningmachine

Developing a Single Hidden Layer Feedforward Neural Network with Extreme Learning Machine concepts

extruct icon extruct

Extract embedded metadata from HTML markup

face-rating icon face-rating

A machine learning model that predicts facial attractiveness from images

face_recognition icon face_recognition

使用webface人脸数据集以及DeepID网络,通过Caffe训练出模型参数,得到LFW二分类的人脸识别准确率。

facemash icon facemash

Service that allows users to rate the attractiveness of people's profile Facebook photos.

faceo icon faceo

face extraction and averaging with python opencv dlib

facerecognition icon facerecognition

Face Recognition using PCA and SVM on Yale, CMU-PIE and SMAI 2013 Student Datasets

facex icon facex

A high performance open source face landmarks detector, based on explicit shape regression algorithm.

facial-emotion-detection-using-convolutional-neural-networks-and-representational-autoencoder-units icon facial-emotion-detection-using-convolutional-neural-networks-and-representational-autoencoder-units

This work showcases two independent methods for recognizing emotions from faces. The first method using representational autoencoder units, a fairly original idea, to classify an image among one of the seven different emotions. The second method uses a 8-layer convolutional neural network which has an original and unique design, and was developed from scratch. The models were trained on the JAFFE dataset, and we tested on a seperate JAFFE test set and a subjectively labeled Labeled Faces in the Wild data set.

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