📸Collect user face data by creating a webcam and detecting the person's face.
🧠Train the model using the LBPH algorithm to recognize faces.
✅Test the face recognition system to ensure it detects and recognizes faces accurately.
📸Collect user face data by creating a webcam and detecting the person's face.
🧠Train the model using the LBPH algorithm to recognize faces.
✅Test the face recognition system to ensure it detects and recognizes faces accurately.
What are the requirements to build the face recognition system?
—You need Python 3.6.5, OpenCV 4.5.2, and a webcam.
How many images should be collected for each user?
—It is recommended to collect at least 500 images per user for better accuracy.
What algorithm is used for the face recognition?
—The LBPH (Local Binary Patterns Histogram) algorithm is used for face recognition.
Can the face recognition system detect unknown faces?
—Yes, the system can detect both known and unknown faces.
How accurate is the face recognition system?
—The accuracy depends on the quality of the collected data and the LBPH algorithm's performance.
00:00Introduction to building a face recognition system using Python and OpenCV.
01:16Step 1: Collect user face data by creating a webcam and detecting the person's face.
01:57Step 2: Train the model using the LBPH algorithm to recognize faces.
02:45Step 3: Test the face recognition system to ensure it detects and recognizes faces accurately.
