Real time gender detection has become a critical component in the new domain of computer human observation and Computer Human Interaction (HCI). Gender detection has numerous applications in the area of recommender systems, focused advertising, security and surveillance. Detection of gender by using the facial features is done by many methods such as Gabor wavelets, artificial neural networks and support vector machine.
In this project, we have used deep learning as a pre-cursor.
- Dataset was downloaded from Kaggle
- Used chrome extension to download images of black men and women with afro.
Below are the list of tools used in building the gender detection model
- Python
- Tensorflow
- OpenCV
- cvlib
- DVC
- Matplotlib
- Pandas
- Numpy
You can set up an environment for the gender detection model by using the following commands:
$ pip install python
$ pip install tensorflow
$ pip install keras
$ pip install cvlib
$ pip install pandas
$ pip install opencv
$ pip install matplotlib
$ pip install tensorflow-hub
DVC is built upon Git and its main goal is to codify data, models and piplines through the command line. Although DVC can work stand-alone, it's highly recommended to work aloginside Git
DVC can be installed as a Python Library with pip package manager:
$ pip install dvc
It is also possible to isntall DVC using conda:
$ conda install -c conda-forge mamba
$ mamba install -c conda-forge dvc
$ mamba install -c conda-forge dvc-s3
After installing DVC, you can go to your project's folder and initialize both Git and DVC:
$ git init
$ dvc init