For the facial recognition part, I used the Eigenface method to recognize visitors' faces with my database and combined it with the previously OpenCV programme I made so it can detect LIVE faces and do the recognition.
As the image above shown (in this example there were five faces in the original database), the programme will take a picture of the visitor's face whenever it detects a face and compares it with the database. (The first three lines of this image shows the progress of facial recognition which I will not show in the final installation.)
After comparing LIVE image with the database:
The video below is a simple example I made to show how it works and I also had used it to find the distance between the same visitor so that I can add the 3rd function above in the final:
As the image above shown (in this example there were five faces in the original database), the programme will take a picture of the visitor's face whenever it detects a face and compares it with the database. (The first three lines of this image shows the progress of facial recognition which I will not show in the final installation.)
After comparing LIVE image with the database:
- show the closest face from the database
- show the distance between the closest face and the LIVE face
- the picture will be added to the database if is not already in the database.
The video below is a simple example I made to show how it works and I also had used it to find the distance between the same visitor so that I can add the 3rd function above in the final:
- The original database in this example contains four images