Gender-and-Age-Detection

Objective

This project is a fork of https://github.com/smahesh29/Gender-and-Age-Detection used to guess the gender and age of a person using machine learning.

Main changes done here:

• Dockerized the app
• Modified the code to accept a video instead of an image. This creates and outpus frames (jpg images) of any and all detected faces in a video. It outputs the those frames into the /frames directory. From there, they could be combined into a video using ffmpeg (see 'Helpful Commandlines' section below).

Dependencies (If not using the docker image)

pip install opencv-python argparse

Usage

Docker build

docker build -t gad .
docker run --rm -it -v <samples-dir>:/samples -v <frames-dir>:/frames gad bash

CLI usage

python detect.py --video <absolute-path-to-video>

Helpful Commandlines

Convert series of images to a video

ffmpeg -framerate 15 -pattern_type glob -i *.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p output.mp4

About the Project

In this Python Project, I had used Deep Learning to accurately identify the gender and age of a person from a single image of a face. I used the models trained by Tal Hassner and Gil Levi. The predicted gender may be one of ‘Male’ and ‘Female’, and the predicted age may be one of the following ranges- (0 – 2), (4 – 6), (8 – 12), (15 – 20), (25 – 32), (38 – 43), (48 – 53), (60 – 100) (8 nodes in the final softmax layer). It is very difficult to accurately guess an exact age from a single image because of factors like makeup, lighting, obstructions, and facial expressions. And so, I made this a classification problem instead of making it one of regression.

Dataset :

For this python project, I had used the Adience dataset; the dataset is available in the public domain and you can find it here. This dataset serves as a benchmark for face photos and is inclusive of various real-world imaging conditions like noise, lighting, pose, and appearance. The images have been collected from Flickr albums and distributed under the Creative Commons (CC) license. It has a total of 26,580 photos of 2,284 subjects in eight age ranges (as mentioned above) and is about 1GB in size. The models I used had been trained on this dataset.

Credit :

** Forked from https://github.com/smahesh29/Gender-and-Age-Detection **