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

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enter/Machine Tips (Quantum)/Resources/Companies, Orgs, & Events/NYC-HAQ 2024.md

@@ -1,4 +1,4 @@
-
+z
 With CUNY, Columbia, & NYU
 in collaboration with Qbraid, SandboxAQ, & QWorld. 
 

enter/Selfie Verification Program High Level Description.md

@@ -0,0 +1,11 @@
+
+
+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).

enter/Step 1. Selfie Verification Program (Front End).md

@@ -0,0 +1,101 @@
+
+
+#### 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.

enter/Step 2. Program for facial feature Comparison.md

@@ -0,0 +1,23 @@
+
+
+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
+}
+
+```