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Noted: Saturday, October 12, 2024, 20:30:01 + 5

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Shwetha Jayaraj 2024-10-12 20:30:01 -04:00
parent c3e94fd086
commit ceeff4d06c
6 changed files with 200 additions and 30 deletions
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enter/Machine Tips (Quantum)/Resources/Companies, Orgs, & Events/NYC-HAQ 2024.md
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z
With CUNY, Columbia, & NYU
in collaboration with Qbraid, SandboxAQ, & QWorld.

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enter/Selfie Verification Program High Level Description.md 100644
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Creating a selfie verification program in JavaScript involves a combination of image processing, machine learning, and user interface design. Here's a step-by-step guide to building a robust selfie verification program:
### 1. **Core Requirements:**
- **Webcam Integration**: To capture selfies from the user's camera.
- **Facial Recognition**: To compare the captured selfie with a pre-existing reference image (e.g., a user profile picture).
- **Machine Learning Model**: For facial recognition and comparison (often using pre-trained models like OpenCV, face-api.js, or external services like AWS Rekognition or Microsoft Azure Face API).
- **Backend Verification**: To handle the logic, store data securely, and interact with a database.
- **User Interface**: To allow the user to take a selfie, view results, and handle errors (e.g., no face detected, low quality).

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enter/Step 1. Selfie Verification Program (Front End).md 100644
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#### A. **Frontend: Capture Selfie**
The frontend will handle capturing the selfie from the user's webcam and sending it to the backend for analysis.
## Step 1: **Set Up Webcam Access:**
- Use the `getUserMedia()` API to access the webcam. This will allow users to capture their selfies in real-time.
```javascript
async function startWebcam() {
const stream = await navigator.mediaDevices.getUserMedia({ video: true });
const video = document.querySelector('#videoElement');
video.srcObject = stream;
}
startWebcam();
```
Within the Flutter app:
```html
<video id="videoElement" autoplay></video>
<canvas id="canvasElement" style="display:none;"></canvas>
```
## Step 2: Capturing the Selfie program:
```javascript
function captureSelfie() {
const video = document.querySelector('#videoElement');
const canvas = document.querySelector('#canvasElement');
const context = canvas.getContext('2d');
const width = video.videoWidth;
const height = video.videoHeight;
canvas.width = width;
canvas.height = height;
context.drawImage(video, 0, 0, width, height);
return canvas.toDataURL('image/png'); // Returns the selfie as a base64-encoded image
}
```
## Step 3: Incorporating the ML Models for the Selfie
1. Incorporate into the HTML first
```html
<script src="https://unpkg.com/face-api.js"></script>
```
2. Then write it into your javascript program
**This is the part to actually research and improve on top of the current existing process**
Function for Loading the Model
```javascript
async function loadFaceAPI() {
await faceapi.nets.ssdMobilenetv1.loadFromUri('/models');
await faceapi.nets.faceLandmark68Net.loadFromUri('/models');
await faceapi.nets.faceRecognitionNet.loadFromUri('/models');
}
```
Function for Comparing the Model to the Captured Selfie:
```javascript
async function detectFace(image) {
const detections = await faceapi.detectSingleFace(image)
.withFaceLandmarks()
.withFaceDescriptor();
return detections;
}
```
Remember to load the "faceapi" into a separate folder.
You will need to upload the face recognition models (`face_landmark`, `ssd_mobilenet`, and `face_descriptor`) from a server or local file system for `face-api.js` to work properly.

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enter/Step 2. Program for facial feature Comparison.md 100644
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If you already have the reference face (like the user profile picture), extract the descriptor of both the selfie and the reference image, and compare them.
```javascript
async function compareFaces(image1, image2) {
const detections1 = await detectFace(image1);
const detections2 = await detectFace(image2);
if (!detections1 || !detections2) {
return false; // No face detected
}
const distance = faceapi.euclideanDistance(
detections1.descriptor, detections2.descriptor
);
const MATCH_THRESHOLD = 0.6; // Set an appropriate threshold
return distance < MATCH_THRESHOLD; // If distance is below the threshold, the faces match
}
```