Our Face Recognition System uses deep learning to detect and recognize faces with high accuracy. Here’s a step-by-step explanation of how we trained the model and how it recognizes faces in real time.
🔧 1. We Collected Face Images
We started by collecting multiple face images of each person we wanted the system to recognize. We captured the photos from different angles and lighting conditions to improve accuracy.
✅ In our demo, we used real images of our office colleagues for this step.
🧠2. We Trained the Model
Next, we trained the model using these images. We used a deep learning architecture like FaceNet or DeepFace, which processes each face to generate a 128-dimensional feature vector (called an embedding).
This vector represents the unique features of each person’s face. The model learned to distinguish one face from another by analyzing these embeddings.
📦 3. We Created a Face Database
After training, we stored the embeddings in a face database, linking each one to a specific name or ID. This database forms the core of the recognition process.
📷 4. We Detected Faces in Real Time
When you use the system, it captures live images through a camera. It then detects all visible faces using algorithms like MTCNN or Haar Cascades.
🧬 5. We Extracted Face Features
For every detected face, the system extracts a fresh embedding — just like it did during training. This step ensures consistency in how we compare faces.
🆔 6. We Matched Faces
We compared the live embedding with the ones stored in our database using cosine similarity or Euclidean distance.
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- If the similarity score crossed a threshold, we confirmed the identity.
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- If it didn’t match any stored face, we marked the person as “Unknown.”
✅ 7. We Displayed the Result
Finally, the system displayed the recognized person’s name or ID on the screen in real time — as shown in our demo video with office colleagues.