iOS | Web |
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Overflow.ai (Legacy version) |
The idea of this project had been in my head for a long time, I couldn't find the time to carry it out. Then the Youtuber DotCSV launched the challenge #RetoDotCSV2080Super where you have to build a project using Pix2Pix model. So perfect timing to start to code.
This was the first big challenge. As you may know there is no dataset for people with open and closed eyes. The dataset Closed Eyes In The Wild(CEW) is very useful but does not have a data labeled by person with open/closed eyes.
So what I propose is to generate the dataset manually with the help of OpenCV. Basically I take a picture from CEW dataset and overlay on it the eyes of a random photo of the UTKFace dataset. Then I manually clean the patches using Photoshop.
I did this process several times until I achieved a final dataset of 48 samples.
GIF: From FacePatcher image to (hopefully) Photoshop level
- In order to achieve better accuracy it is necessary to increase the dataset samples and this way try with more interesting approaches to solve the task.
- Currently the output of the app is a 256x256 picture. The output is only for the face, so a nice thing to do would be to overlay the result on the original photo.
Developed by Jesús Alberto MartÃnez Mendoza.
Authors: Zhang, Zhifei, Song, Yang, and Qi, Hairong.
https://susanqq.github.io/UTKFace/
F.Song, X.Tan, X.Liu and S.Chen, Eyes Closeness Detection from Still Images with Multi-scale Histograms of Principal Oriented Gradients, Pattern Recognition, 2014.
The MIT License (MIT)
Copyright (c) 2018 Jesús Alberto MartÃnez Mendoza(@jesusmartinoza)
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