DEV Community 👩‍💻👨‍💻

DEV Community 👩‍💻👨‍💻 is a community of 966,904 amazing developers

We're a place where coders share, stay up-to-date and grow their careers.

Create account Log in
Cover image for Facial recognition + good faces bot = best faces 🏆
Bryce Dorn
Bryce Dorn

Posted on

Facial recognition + good faces bot = best faces 🏆

@good_faces_bot is probably the best Twitter account there is. It's pretty self-explanatory: just a lot of good faces posted regularly. I wanted to sift through the endless stream to find the best faces and this was the result:

Get the faces 🐦

This part doesn't need much explaining, everyone with basic coding experience knows how to and probably has fetched from Twitter's API. Borrowing from the timelines quick-start guide I got an API token and downloaded all the tweets for the account.

After that, it sorts them and filters for tweets above a certain threshold of likes (1000). Then the last thing to do is to download the image associated with each tweet with a slight staggering to avoid getting rate-limited and it's ready to be analyzed!

Enter: rustface 🔬

Put simply, this is a Rust-y wrapper around the SeetaFace Engine, an excellent open-source tool for real-time facial recognition. It works surprisingly well given applications like this generally need massive amounts of processing power. Can read more about it here.

The included example was very helpful; I modified it to loop over the list of images and made it so instead of drawing a rectangle over the face it outputs a cropped image of the face. With some minor error-handling it was making it through all of the images in no time!

Unfortunately it only recognizes a face in about 1/3 of all the images currently (and some of the "faces" aren't actually faces, like the first one on the page 😂). With some tweaking this could be improved though.

Putting it all together 🪢

Now that the Rust part is complete, all that's left is to display the faces somehow. I used Svelte to build a simple site since I like using it for small projects like this.

The trickiest part here was that although the Rust step generated a bunch of images, there isn't an easy way to connect them back to each tweet. I used fs.exists to loop over the output directory and generate a bunch of imports & exports. It's hacky but ended up working well! But beware 👻 you shouldn't be generating raw javascript like this unless there are no other options, here's a snippet:

const hasOne = await fsExists(generatePath(tweet));

if (hasOne) {

facePaths.forEach(facePath => {
  output += `import ${faceFileName} from "../${facePath}";\n`;

output += `export const facesFor${} = [${faceFileNames.join(',')}];\n`;

await fs.appendFile(outputFile, output);
Enter fullscreen mode Exit fullscreen mode

Anyways once that was done I could loop over the list of tweets from the first step and display them in Svelte, referencing the facesForX import directly:

{#each tweets as tweet}
    {#if faces[`facesFor${}`].length > 0}
        {#each faces[`facesFor${}`] as face, i}
            <Face face={face} />
Enter fullscreen mode Exit fullscreen mode

Note that it checks if length > 0: since it isn't able to detect a face in every image, unfortunately some get left behind. It's sad too since a lot of the really good faces are the stranger looking ones.

Then I added a simple hover state to show the original image that the face was detected in:

    export let tweet;
    export let face;

    export let show = () => showOriginal = true;
    export let hide = () => showOriginal = false;

    let showOriginal = false;

<a href={tweet.tweet.text}>
    <img src={showOriginal ? : face} on:mouseenter={show} on:mouseleave={hide} />
Enter fullscreen mode Exit fullscreen mode

That's pulled directly from the Svelte component too! No wrappers or boilerplate, part of the reason why it's so satisfying to use for development.

Et voilà! The last kicker is that since all of these are just scripts, they can run sequentially in a GitHub Action! Although the facial recognition part takes a while (~20m) the simpler ones can fetch/generate the necessary data and run entirely in CI. There are some optimizations to be done here around uploading/downloading artifacts but in the meantime I'm happy it all works remotely.

Check out the code here:

GitHub logo brycedorn / best-faces

Using rustface to determine good_faces_bot's best faces


@good_faces_bot is probably the best Twitter account there is. It's pretty self-explanatory: just a lot of good faces posted regularly. I wanted to sift through the endless stream to find the best faces and this was the result!


Running locally

First create a .env file with your BEARER_TOKEN for accessing Twitter's API and the USER_ID for the account you want to fetch tweets for.

Then, in the following order:

npm install
npm run fetch:tweets
npm run sort:tweets
npm run fetch:images

And for rust:

cargo install
cargo run

Finally for the site:

npm run generate:imports
npm run dev

Latest comments (0)

This post blew up on DEV in 2020:

js visualized

🚀⚙️ JavaScript Visualized: the JavaScript Engine

As JavaScript devs, we usually don't have to deal with compilers ourselves. However, it's definitely good to know the basics of the JavaScript engine and see how it handles our human-friendly JS code, and turns it into something machines understand! 🥳

Happy coding!