All-In-One

All in one paper implementation

Getting started

All in one convolutional network for face analysis presents a multipurpose algorithm for simultaneous face detection, face alignment, pose estimation, gender recognition, smile detection, age estimation and face recognition using a single convolutional neural network(CNN).

Prerequisites

The project can be run by installing conda virtual environment with python=3.6 and installing dependencies using pip. Inside the projects directory run the following commands.

• conda create -n <environment_name> python=3.6.
  After creating the environment use the following commands to install dependacies.
• pip install keras
• pip install tensorflow
• pip install sklearn
• pip install pandas
• pip install opencv-python
• pip install dlib

Download pretrained model

cd models
./install.sh

Setup

  run the following command to generate gRPC classes for Python
  # only in Service folder run
  $ python3.6 -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. Service/service_spec/all_in_one.proto

Usage

To run it on your own image, use the following command. Please make sure to see their paper / the code for more details.

 # on project directory this will start the server 
   $ python  start_service.py

Using docker with CPU

You can also build an image which has only the CPU dependecies to evaluate the models provided.

     docker build --file Dockerfile . -t singnet:all_in_one_cpu

How to Use the docker image

  # this will open port 50051 and run the service 
  docker run -it --rm -p 50051:50051 singnet:all_in_one_cpu

How to train the model

Three datasets are used for training the network.

• AFLW dataset provides a large-scale collection of annotated face images gathered from the web, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions.
• IMDB-WIKI dataset is the largest publicly available dataset of face images with gender and age labels for training.
• CelebA dataset is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations.
• Adience dataset attempts to capture all the variations in appearance, noise, pose, lighting and more, that can be expected of images taken without careful preparation or posing.

The network architecture can be found in this paper. The model is built with deep convolutional layers in keras and is found in nets/model.py.

Training

The model can be trained with age, gender,detection, visibility,pose, landmarks,identity, smile, and eye_glasses labels by using the following commands inside the project's directory.
The following code snippet is bash command to train the network in aflw dataset for face detection

python -m train --dataset aflw --images_path /path-to-dataset-images/ \
    --label detection --batch_size 100 --steps 500  --ol output-of-large-model --os output-of-small-model --epochs 10;

Options

• --images_path - Path to dataset images
• --dataset - Type of dataset to train the model. This could be imdb, wiki, celeba,yale,ck+,aflw. The layers that are going to be trained also depends on this choice.
• --label - This option specifies which for which type of classification/prediction to train the model. The choices are age, gender,detection, visibility,pose, landmarks,identity, smile, and eye_glasses.
• --epochs.
• --batch_size.
• --resume - To start training from previous checkpoint if available.
• --steps - Steps per epoch.
• --ol - Output filename to save large model(model with all layers)
• --os - Output filename to save small model(model with layers trained with current training)
• --load_model -
• --freeze - If true freezes shared layers of the model

How to run demo

To do lists

Previous results recorded after training the model.

• Gender estimation(~89% accuracy)
• Face detection(~90% accuracy)
• Smile detection(~91% accuracy)
• Age prediction(~4% accuaracy)

Tasks remaining

• Use CASIA and MORPH dataset for further training the model on age, detection and gender labels.
• Implement pose estimation, Landmark detection and Face recognition.