In order to engage users for longer term relationships it's important to be able to tell if you've met the user before, and recall past engagements.

• Need to be able to tell which user is which
• Replacement for rfid (which is too short range for unobtrusive use)
• Distinguish between a few people (<10)
• Use the camera and OpenCV's haar face finder
• Communicate over yarp

Also provide a test competency to try out reusable standard stuff for lirec (yarp, cross platform development, etc)

• Using squared sum of differences (SSD) to compare stored images with any faces found in the image.
• Faces can be any size in the image, but are smoothly scaled into a standard resolution for comparison and storage
• Two modes of operation

Look for all faces in the image, compare with stored faces and create a new identity if the difference is over a given threshold. Do this each frame until switched into detect mode.

• Uses a maximum count of faces, in order to prevent storing too many false detections

Look for all faces in the image, compare with stored faces and print the id number of the most similar stored face, if it's over a given threshold.

* Adding user inteface for recording up to 10 people by pressing the number keys * Better output - rendering the recorded faces, and draws lines between them and the detected faces.

Added a simple message to tell other programs which user is in view, with a confidence metric.

The program still needs to be driven by user input, calibrating it by recording people at startup.

Rather than simply outputting the user id's every frame, it's more useful if the program tracks the state of the scene internally, and outputs changes. I've added the following yarp messages:

New messages

Loading and saving is done quickly, for the review - and just consists of saving images as png files and a quickly hacked together ascii format for mapping the images to the id numbers.

Persistence across time via load and save to disk brings up problems - the computer vision will break when lighting changes too much, the saved faces will be too different from the visible ones. One possible workaround would be to lower the error threshold and sneakily recalibrate for the most likely user at the start of a new session.

Due to a request from INESC-ID, which helps thinking about this more in terms of the wider architecture, it's important that competencies can be tured on or off easily, particularly if they use up cpu, as computer vision competencies will. To this end I've added an idle command:

Using TinyXML as a source library for making the persistence much more scalable and user editable.

I've also added another yarp message:

This is the first step to dealing with lighting changes, just allowing images of people in multiple lighting situations to be used at once. The system looks through each image for each person in order to match the closest.

In calibration mode, the system will add new images to the user if the detected ones are above the error threshold. This is slightly dangerous, as you would be able to get images for different people in the same id… Not sure if there is a way to cope with that automatically.