diff --git a/.gitignore b/.gitignore
index 36fd3ed7..1bfa58fb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -26,4 +26,3 @@ tf_models/
 # In-Development Commands
 commands/edit-face/danny-devito.js
 commands/edit-face/emoji-face.js
-commands/edit-face/eyes.js
diff --git a/Xiao.js b/Xiao.js
index a420a670..4f962f55 100644
--- a/Xiao.js
+++ b/Xiao.js
@@ -210,6 +210,10 @@ client.on('ready', async () => {
 		client.logger.error(`[NSFW MODEL] Failed to load NSFW model\n${err.stack}`);
 	}
 
+	// Set up face detection
+	await client.loadFaceDetector();
+	client.logger.info('[FACE DETECTOR] Loaded face detector.');
+
 	// Fetch all members
 	for (const [id, guild] of client.guilds.cache) { // eslint-disable-line no-unused-vars
 		await guild.members.fetch();
diff --git a/commands/edit-face/anime-eyes.js b/commands/edit-face/anime-eyes.js
index da38e6c6..5032eea3 100644
--- a/commands/edit-face/anime-eyes.js
+++ b/commands/edit-face/anime-eyes.js
@@ -1,9 +1,6 @@
 const Command = require('../../framework/Command');
 const request = require('node-superfetch');
 const { createCanvas, loadImage } = require('canvas');
-const tfnode = require('@tensorflow/tfjs-node');
-const faceDetection = require('@tensorflow-models/face-detection');
-const model = faceDetection.SupportedModels.MediaPipeFaceDetector;
 const path = require('path');
 
 module.exports = class AnimeEyesCommand extends Command {
@@ -26,16 +23,13 @@ module.exports = class AnimeEyesCommand extends Command {
 				}
 			]
 		});
-
-		this.detector = null;
 	}
 
 	async run(msg, { image }) {
-		if (!this.detector) this.detector = await faceDetection.createDetector(model, { runtime: 'tfjs', maxFaces: 10 });
 		const leftEye = await loadImage(path.join(__dirname, '..', '..', 'assets', 'images', 'anime-eyes', 'left.png'));
 		const rightEye = await loadImage(path.join(__dirname, '..', '..', 'assets', 'images', 'anime-eyes', 'right.png'));
 		const imgData = await request.get(image);
-		const faces = await this.detect(imgData.body);
+		const faces = await this.client.detectFaces(imgData.body);
 		if (!faces) return msg.reply('There are no faces in this image.');
 		if (faces === 'size') return msg.reply('This image is too large.');
 		const base = await loadImage(imgData.body);
@@ -56,15 +50,4 @@ module.exports = class AnimeEyesCommand extends Command {
 		}
 		return msg.say({ files: [{ attachment: canvas.toBuffer(), name: 'anime-eyes.png' }] });
 	}
-
-	async detect(imgData) {
-		if (Buffer.byteLength(imgData) >= 4e+6) return 'size';
-		tfnode.setBackend('tensorflow');
-		const image = tfnode.node.decodeImage(imgData);
-		tfnode.setBackend('cpu');
-		const faces = await this.detector.estimateFaces(image);
-		tfnode.setBackend('tensorflow');
-		if (!faces || !faces.length) return null;
-		return faces;
-	}
 };
diff --git a/commands/edit-face/eyes.js b/commands/edit-face/eyes.js
new file mode 100644
index 00000000..5b9e98bf
--- /dev/null
+++ b/commands/edit-face/eyes.js
@@ -0,0 +1,51 @@
+const Command = require('../../framework/Command');
+const request = require('node-superfetch');
+const { createCanvas, loadImage } = require('canvas');
+const path = require('path');
+
+module.exports = class EyesCommand extends Command {
+	constructor(client) {
+		super(client, {
+			name: 'eyes',
+			group: 'edit-face',
+			memberName: 'eyes',
+			description: 'Draws emoji eyes onto the faces in an image.',
+			throttling: {
+				usages: 1,
+				duration: 60
+			},
+			args: [
+				{
+					key: 'image',
+					prompt: 'What face would you like to scan?',
+					type: 'image-or-avatar'
+				}
+			]
+		});
+	}
+
+	async run(msg, { image }) {
+		const eyes = await loadImage(path.join(__dirname, '..', '..', 'assets', 'images', 'eyes.png'));
+		const imgData = await request.get(image);
+		const faces = await this.client.detectFaces(imgData);
+		if (!faces) return msg.reply('There are no faces in this image.');
+		if (faces === 'size') return msg.reply('This image is too large.');
+		const base = await loadImage(imgData.body);
+		const canvas = createCanvas(base.width, base.height);
+		const ctx = canvas.getContext('2d');
+		ctx.drawImage(base, 0, 0);
+		for (const face of faces) {
+			const eyeWidth = face.box.width / 4;
+			const eyeHeight = face.box.height / 4;
+			const leftEyeData = face.keypoints.find(landmark => landmark.name === 'leftEye');
+			const rightEyeData = face.keypoints.find(landmark => landmark.name === 'rightEye');
+			const leftEyeX = leftEyeData.x - (eyeWidth / 2);
+			const leftEyeY = leftEyeData.y - (eyeHeight / 2);
+			const rightEyeX = rightEyeData.x - (eyeWidth / 2);
+			const rightEyeY = rightEyeData.y - (eyeHeight / 2);
+			ctx.drawImage(eyes, leftEyeX, leftEyeY, eyeWidth, eyeHeight);
+			ctx.drawImage(eyes, rightEyeX, rightEyeY, eyeWidth, eyeHeight);
+		}
+		return msg.say({ files: [{ attachment: canvas.toBuffer(), name: 'eyes.png' }] });
+	}
+};
diff --git a/structures/Client.js b/structures/Client.js
index a128eaf6..9c79ef20 100644
--- a/structures/Client.js
+++ b/structures/Client.js
@@ -4,6 +4,9 @@ const { Collection } = require('@discordjs/collection');
 const winston = require('winston');
 const fontFinder = require('font-finder');
 const nsfw = require('nsfwjs');
+const tfnode = require('@tensorflow/tfjs-node');
+const faceDetection = require('@tensorflow-models/face-detection');
+const model = faceDetection.SupportedModels.MediaPipeFaceDetector;
 const moment = require('moment-timezone');
 const fs = require('fs');
 const url = require('url');
@@ -38,6 +41,7 @@ module.exports = class XiaoClient extends CommandClient {
 		this.activities = activities;
 		this.adultSiteList = null;
 		this.nsfwModel = null;
+		this.faceDetector = null;
 	}
 
 	async loadParseDomain() {
@@ -82,6 +86,22 @@ module.exports = class XiaoClient extends CommandClient {
 		return this.nsfwModel;
 	}
 
+	async loadFaceDetector() {
+		this.faceDetector = await faceDetection.createDetector(model, { runtime: 'tfjs', maxFaces: 10 });
+		return this.faceDetector;
+	}
+
+	async detectFaces(imgData) {
+		if (Buffer.byteLength(imgData) >= 4e+6) return 'size';
+		tfnode.setBackend('tensorflow');
+		const image = tfnode.node.decodeImage(imgData);
+		tfnode.setBackend('cpu');
+		const faces = await this.faceDetector.estimateFaces(image);
+		tfnode.setBackend('tensorflow');
+		if (!faces || !faces.length) return null;
+		return faces;
+	}
+
 	fetchReportChannel() {
 		if (!REPORT_CHANNEL_ID) return null;
 		return this.channels.fetch(REPORT_CHANNEL_ID);