kenh1991 / faces

Faces is project for multiple models detection from faces such as: Gender, Expressions, Illumination, Pose, Occlusion, Age, Makeup

Faces

Faces is project for multiple models detection from faces such as:

1. Gender : Male, Female
2. Expressions : Anger, Happiness, Sadness, Surprise, Fear, Disgust
3. Illumination : Bad, Medium, High
4. Pose : Frontal, Left, Right, Up, Down
5. Occlusion : Glasses, Beard, Ornaments, Hair, Hand, None, Others
6. Age : Child, Young, Middle and Old
7. Makeup : Partial makeup, Over-makeup

this video test Faces project on part of video of End Game trailer, and here some examples of prediction on images

Requirements

• Python 3.*
• Imutils
• Numpy
• OpenCV
• Pytorch

Run

to use Faces execute run.py file with various options usage: run.py [-h] [--cuda CUDA] [--show SHOW] [--delay DELAY] [--inputs_path INPUTS_PATH] [--outputs_path OUTPUTS_PATH] [--models_path MODELS_PATH] [--models MODELS [MODELS ...]]

optional arguments:
* -h, --help            show this help message and exit

* --cuda CUDA           set this parameter to True value if you want to use cuda gpu, default is True
* --show SHOW           set this parameter to True value if you want display images/videos while processing, default is True
* --delay DELAY         amount of seconds to wait to switch between images while show the precess
* --inputs_path INPUTS_PATH path for directory contains images/videos to process, if you don't use it web-cam will open to start the record
* --outputs_path OUTPUTS_PATH path for directory to add the precesses images/videos on it, if you don't use it output directory will created and add the precesses images/videos on it
* --models_path MODELS_PATH path for directory contains pytorch model
* --models MODELS [MODELS ...] first index refers to gender model, second index refers to expression model, and third index refers to multiple models Ex: gender, multiple ->> 1,0,1 we set expression to 0 to not use it, default 1,1,1

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Datasets

• IMDB ~ 500K image
• WIKI ~ 60K image
• FER ~ 35K image
• IMFDB ~ 3K image
• JAFFE ~ 250 image

Models

models in this project are based on Real-time Convolutional Neural Networks for Emotion and Gender Classification paper model architecture:

project consist of 3 models:

1. Gender
2. Expression
3. Multiple

Gender Model

trained done by using IMDB , WIKI, IMFDB datasets. consist of ~ 600 K image, and by using tencrop data augmentation dataset increased to be ~ 6 M image Accuracy ~ 78% using only 6 epochs and it will reach higher accuracy expected to be ~ 96 %

Expression Model

trained done by using FER , IMFDB, JAFFE image datasets. consist of ~ 40 K image, and by using tencrop data augmentation dataset increased to be ~ 400 K image Accuracy ~ 60% using 15 epochs and it will reach higher accuracy expected to be ~ 66 %

Multiple Models

this model is little bit different form Gender & Expression models in this model we use one feature extractor model and 5 different classifiers each classifier predict specific features form faces and they are:

• Illumination : Bad, Medium, High
• Pose : Frontal, Left, Right, Up, Down
• Occlusion : Glasses, Beard, Ornaments, Hair, Hand, None, Others
• Age : Child, Young, Middle and Old
• Makeup : Partial makeup, Over-makeup

trained done by using IMFDB image datasets consist of ~ 3 K image, and by using tencrop data augmentation dataset increased to be ~ 30 K image Accuracy ~ 77% using 15 epochs

Train

all training process done on Faces notebook using Google Colab cloud

Credits

Real-time Convolutional Neural Networks for Emotion and Gender Classification paper