Gender and Emotion Classifier with Deep Learning

A classifier that uses realtime webcams to detect faces and extrapolate emotions and gender of faces

Guided back-prop

Real-time demo:

Real-time face detection and emotion/gender classification using fer2013/IMDB datasets with a keras CNN model and openCV.

IMDB gender classification test accuracy: 96%.
fer2013 emotion classification test accuracy: 66%.

Inspiration

This project is greatly inspired from the Sandberg Paper called Google FaceNet, thereby harnessing Google's whitepaper implementations and applying them for real world production ready use cases and environments as a robust solution

Technology

Oversight uses a number of open source projects to work properly:

Tensorflow - A google open-source ML framework
Python - awesome language we love

Environment Setup

This was built on Windows 10.

These were the pre-requisities :

NVIDIA CUDA Toolkit

CUDA - parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). Download and Install all the patches. During install, choose Custom and uncheck the Visual Studio Integration checkbox.

Download cuDNN

cuDNN - The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. Create a NVIDIA developer account to download.

Set Path :

Add the following paths, C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\libnvvp C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin\extras\CUPTI\libx64

Install Anaconda with 3.6 x64

$ conda update conda

Run package installer

$ pip install -r requirements.txt

Instructions

Run real-time emotion demo:

$ python3 video_emotion_color_demo.py

Run real-time guided back-prop demo:

$ python3 image_gradcam_demo.py

Make inference on single images:

$ python3 image_emotion_gender_demo.py <image_path>

e.g.

$ python3 image_emotion_gender_demo.py ../images/test_image.jpg

To train previous/new models for emotion classification:

Download the fer2013.tar.gz file from here
Move the downloaded file to the datasets directory inside this repository.
Untar the file:

$ tar -xzf fer2013.tar

Run the train_emotion_classification.py file

$ python3 train_emotion_classifier.py

To train previous/new models for gender classification:

Download the imdb_crop.tar file from here (It's the 7GB button with the tittle Download faces only).
Move the downloaded file to the datasets directory inside this repository.
Untar the file:

$ tar -xfv imdb_crop.tar

Run the train_gender_classification.py file

$ python3 train_gender_classifier.py

Todos

Optimize Further to increase speed
Implement Docker and Jenkins based deployment

License

Public

pourabkarchaudhuri / gender-emotion-classifier Goto Github PK

gender-emotion-classifier's Introduction