HappyNet

by danduncan

danduncan / HappyNet

Convolutional neural network that does real-time emotion recognition. HappyNet detects faces in vide...

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Source code for HappyNet Real-Time Human Emotion Recognition with Convolutional Neural Nets Demo video: https://youtu.be/MDHtzOdnSgA

Authors: Dan Duncan Gautam Shine Chris English

Stanford CS231n - March 2016

About HappyNet: HappyNet takes pictures or webcam video as input. It detects all faces in each frame, and then classifies which emotion each face is expressing.

HappyNet then replaces each face with an emoji corresponding to that emotion.

Recognized emotions: Neutral Happy Sad Angry Surprise Fear

Due to legacy reasons, HappyNet tries to classify a seventh emotion: disgust. We usually combine and angry, as HappyNet couldn't tell these two apart. With more training, maybe it could distinguish.

Training accuracy was 91% and test accuracy was 57%, with the following requirements: - User's facial expression must be strong / exaggerated - Lighting must be good (no shadows on face) - Camera is at eye level or slightly above eye level

The scripts in this repository allow you to: - Run HappyNet - Retrain HappyNet on new data - Automatically generate your own training set

Credits:

This was built using the Caffe and OpenCV libraries

We used the Emotions in the Wild (EitW) Caffe model as our starting point - This model is available on Caffe Model Zoo - EitW is itself based on VGG_S Net

We retrained EitW on new data: Cohn-Kanade Plus (CK+) dataset (available for online download) Japanese Female Facial Expressions (JAFFE) dataset (also available online) Our own data, generated using our 'gather_training_data' script

Requirements: > 2 GB of memory Caffe and OpenCV installed Webcam Note: Webcam currently does not work on virtual machines. Try using a native Mac or Linux system. Don't try on a virtual machine running on Windows.

If you want to run on GPU:
    2GB or more VRAM
    CUDA and CuDNN libraries installed
    Caffe must be compiled with these CUDA and CuDNN selected

Description of files:

Main scripts: gathertrainingdata.py - Use this to generate a custom training set processdataset.py - Read in an entire training set and calculate accuracy over the set processimage.py - Read in a single image, add the correct emoji, and write to file videogenerate.py - Run HappyNet in real-time and save output to video videotest.py - Run HappyNet in real-time; does not save to video

Scripts for retraining the network on new data:

These 5 scripts are to be run in numerical order
Note you'll need to modify them with your own paths

Generate Caffe-compatible database of input images: execute_0_create_file_list exceute_1_create_lmdb_databse

Generate a mean image (mean.binaryproto file) from input dataset: execute_2_create_mean_image

Retrain an existing caffe model with the new inputs: execute_3_train_custom_model

* Note, this needs to be modified if you are running on GPU. Our network was
VGG_S net, which requires 2 GB of GPU memory, so we ran on CPU.
The modification is just an extra flag, something like "-gpu 0"

Delete all unnecessary files execute_4_cleanup_training_data

This deletes the output files from scripts 0, 1, and 2. 
Run this when you are getting ready to start over from file 0.
Don't run it until then though - you might want to reuse the info in those files!

Utility functions: caffefunctions.py - anything dealing primarily with caffe opencvfunctions.py - anything dealing primarily with opencv utility_functions.py - General functions mostly related to file I/O

Datasets: Only contains the emojis we used. Cohn-Kanade Plus (CK+) and Japanese Female Facial Expressions (JAFFE) can be downloaded online.

Models: deploy.prototxt - Architecture of our model (this file should not need to be changed) solver.prototxt - This configures the retraining process. train.prototxt - This configures the architecture during training. Mainly used to add layer-specific learning rates. loss_history.txt - Log file from our last retraining on our dataset

NOT INCLUDED: EmotiWVGGS.caffemodel - This is the file with all the weights. It is 500 MB and cannot be archived. However, you can download the Emotios in the Wild model from Caffe Model Zoo. Retrain it on new data for a day or two, and you can get similar numbers to our model. We trained on: Homebrewed dataset of 2000 images of 5 people making all 6 emotions All data was generated with the script generatetrainingdata.py We trained for: About 24 hours A bigger dataset could be collected in a couple hours and would likely greatly improve performance.

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