feat: Improve face recognition accuracy and speed by adjusting server tolerance, filtering matches, and updating real-time recognition interval.

ai_face_recognition_server/README.md

@@ -0,0 +1,61 @@
+# AI Face Recognition Server
+
+A Flask-based standalone server for face recognition, designed for integration with Odoo Point of Sale (POS).
+
+## Features
+- **Real-time Recognition**: Processes images and returns matches with metadata (ID, Name, Probability).
+- **Face Training**: API endpoint to register new faces from base64 images.
+- **Production Ready**: Supports Gunicorn for stability and concurrency.
+- **Optimized for POS**: Default 60% match threshold handles varied lighting conditions well.
+
+## Prerequisites
+- Python 3.10+
+- System libraries for `dlib` (cmake, g++, etc.)
+- Virtual environment (recommended)
+
+## Installation
+
+1. **Activate the Virtual Environment**:
+   ```bash
+   source .venv/bin/activate
+   ```
+
+2. **Install Dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+## Running the Server
+
+### Production (Recommended)
+Run using Gunicorn for better performance and stability:
+```bash
+.venv/bin/gunicorn -w 1 -b 0.0.0.0:5000 app:app --timeout 120
+```
+
+### Development
+```bash
+python app.py
+```
+
+## API Endpoints
+
+### 1. Recognize Face
+- **Endpoint**: `POST /recognize`
+- **Body**: `{ "image": "base64_string_without_header" }`
+- **Response**: List of matches with `id`, `name`, and `probability`.
+
+### 2. Train Face
+- **Endpoint**: `POST /train`
+- **Body**: `{ "partner_id": 123, "name": "John Doe", "images": ["base64_1", "base64_2"] }`
+- **Description**: Saves images to the `faces/` directory and updates the in-memory model.
+
+## Matching Logic
+- **Tolerance**: `0.4` (Distance)
+- **Probability**: `1.0 - distance` (Matches >= 60% are returned)
+- **Diagnostics**: Server logs the exact distance and probability to the terminal for every request.
+
+## Directory Structure
+- `app.py`: Main Flask application.
+- `faces/`: Directory where trained face images are stored.
+- `requirements.txt`: Python dependencies.

ai_face_recognition_server/app.py

@@ -82,8 +82,8 @@ def recognize_face():
         matches_list = []
 
         for face_encoding in face_encodings:
-            # See if the face is a match for the known face(s)
-            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+            # Use threshold of 0.4 for slightly less strict matching (around 60% match)
+            matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.4)
             name = "Unknown"
             p_id = 0
             probability = 0.0
@@ -96,15 +96,18 @@ def recognize_face():
                     name = known_face_names[best_match_index]
                     p_id = known_face_ids[best_match_index]
                     # Simple probability estimation based on distance (lower distance = higher prob)
-                    # Distance 0.0 -> 100%, Distance 0.6 -> ~0% (threshold)
+                    # Distance 0.0 -> 100%, Distance 0.3 -> 70%
                     distance = face_distances[best_match_index]
                     probability = max(0, 1.0 - distance)
+                    
+                    print(f"Best match for current face: {name} (ID: {p_id}) with distance {distance:.3f} (Prob: {probability:.3f})")
 
-                    matches_list.append({
-                        'id': p_id,
-                        'name': name,
-                        'probability': probability
-                    })
+                    if probability >= 0.6:
+                        matches_list.append({
+                            'id': p_id,
+                            'name': name,
+                            'probability': probability
+                        })
 
         # Sort by probability
         matches_list.sort(key=lambda x: x['probability'], reverse=True)
@@ -165,6 +168,7 @@ def train_face():
 
     return jsonify({'message': f'Processed {processed_count} images for {name}'})
 
+load_known_faces()
+
 if __name__ == '__main__':
-    load_known_faces()
     app.run(host='0.0.0.0', port=5000)

ai_face_recognition_server/requirements.txt

@@ -3,3 +3,5 @@ flask-cors
 face_recognition
 numpy
 opencv-python
+setuptools
+gunicorn

static/src/js/face_recognition_sidebar.js

@@ -59,17 +59,17 @@ export class FaceRecognitionSidebar extends Component {
     startRealTimeRecognition() {
         console.log("startRealTimeRecognition called");
         console.log("Server URL:", this.pos.config.pos_face_rec_server_url);
-        
+
         if (!this.pos.config.pos_face_rec_server_url) {
             console.error("Face Recognition Server URL is not configured. Please configure it in POS Settings.");
             return;
         }
-        
-        // Capture frame every 5 seconds
+
+        // Capture frame every 3 seconds
         this.recognitionInterval = setInterval(() => {
             console.log("Recognition interval tick");
             this.captureAndSendFrame();
-        }, 5000);
+        }, 3000);
     }
 
     async captureAndSendFrame() {
@@ -78,7 +78,7 @@ export class FaceRecognitionSidebar extends Component {
             console.error("Video element not found");
             return;
         }
-        
+
         if (!this.pos.config.pos_face_rec_server_url) {
             console.error("Face Recognition Server URL is not configured");
             return;
@@ -103,14 +103,19 @@ export class FaceRecognitionSidebar extends Component {
             if (response.ok) {
                 const result = await response.json();
                 if (result.matches && result.matches.length > 0) {
+                    // Filter matches (ensure only >= 60% probability)
+                    const filteredMatches = result.matches.filter(match => match.probability >= 0.6);
+
                     // Enrich matches with partner phone numbers
-                    this.state.matches = result.matches.map(match => {
+                    this.state.matches = filteredMatches.map(match => {
                         const partner = this.pos.db.get_partner_by_id(match.id);
                         return {
                             ...match,
                             phone: partner ? partner.phone || partner.mobile : null
                         };
                     });
+                } else {
+                    this.state.matches = [];
                 }
             }
         } catch (err) {
@@ -119,13 +124,18 @@ export class FaceRecognitionSidebar extends Component {
     }
 
     async selectCustomer(match) {
+        console.log("selectCustomer called with match:", match);
         const partner = this.pos.db.get_partner_by_id(match.id);
+        console.log("Partner found in DB:", partner);
         if (partner) {
             this.state.selectedCustomer = partner;
             this.pos.get_order().set_partner(partner);
+            console.log("Partner set to current order:", this.pos.get_order().get_partner());
 
             // Fetch details
             await this.fetchCustomerDetails(partner.id);
+        } else {
+            console.error("Partner not found for ID:", match.id);
         }
     }
 
@@ -136,7 +146,7 @@ export class FaceRecognitionSidebar extends Component {
             ['name', 'date_order', 'amount_total', 'state', 'lines'],
             { limit: 2, order: "date_order desc" }
         );
-        
+
         // Fetch order lines for each order
         for (let order of orders) {
             if (order.lines && order.lines.length > 0) {
@@ -154,7 +164,7 @@ export class FaceRecognitionSidebar extends Component {
                 order.lines = [];
             }
         }
-        
+
         this.state.lastOrders = orders;
 
         // Fetch top 3 products
@@ -163,7 +173,7 @@ export class FaceRecognitionSidebar extends Component {
             console.log("Top products received:", topProducts);
             console.log("Type:", typeof topProducts);
             console.log("First product:", topProducts && topProducts[0]);
-            
+
             // Ensure it's an array
             if (Array.isArray(topProducts)) {
                 this.state.topProducts = topProducts;

static/src/xml/face_recognition_screens.xml

@@ -22,7 +22,7 @@
                     <h4>Matches:</h4>
                     <ul class="match-list">
                         <t t-foreach="state.matches" t-as="match" t-key="match.id">
-                            <li t-on-click="() => this.selectCustomer(match)" class="match-item">
+                            <li t-on-click="() => this.selectCustomer(match)" class="match-item" style="cursor: pointer; background: rgba(0,0,0,0.05); margin-bottom: 5px; padding: 5px;">
                                 <div class="match-info">
                                     <div>
                                         <span class="match-name"><t t-esc="match.name"/></span>
@@ -84,7 +84,7 @@
                         <ul t-if="state.topProducts and state.topProducts.length > 0">
                             <t t-foreach="state.topProducts" t-as="prod" t-key="prod_index">
                                 <li>
-                                    <t t-out="prod['name']['en_US']"/> (Qty: <t t-out="Math.round(prod['qty'])"/>)
+                                    <t t-out="prod['name']['en_US'] || prod['name']"/> (Qty: <t t-out="Math.round(prod['qty'])"/>)
                                 </li>
                             </t>
                         </ul>