As an example of a face pose estimation system, we introduce the Intelligent Shelf project.  In this project, we envision a grocery-store shelf lined with small, unobtrusive cameras.  As a person peruses the contents of the shelf, the cameras would detect the pose of the person's face and estimate where on the shelf they are looking.  This information can be used to improve the ergonomic design of stores and improve the layout of items within the store, to make things easier for customers to find.

Figure 1: Camera frame representing the Intelligent Shelf

As an initial prototype, we have constructed a vertical frame, shown in Figure 1, which represents the shelf, on which we have mounted twelve cameras.  These cameras capture a multi-view sequence of images of persons in front of the camera wall, shown in Figure 2.

Figure 2: Example images from the cameras

To be used in large camera network like the one required for the Intelligent Shelf project, distributed processing of the camera images is essential.  In this project, we are experimenting with techniques for efficiently estimating the pose of each person's face, minimizing the bandwidth requirements while maintaining the accuracy of the detections.

In our current system, estimates of each face's position and orientation are sent to a central evidence accumulation coordinator, which produces a rough pose estimate of the head.  This evidence accumulation framework eliminates false detections and improves the accuracy of the pose estimates.  The accumulated head positions are displayed in a graphical user interface (GUI) (See Figure 3)

Figure 3: Graphical user interface showing the estimated face position for the images shown in figure 2.

    •    Josiah Yoder

    •    Hidekasu Iwaki

    •    Johnny Park

[ogg] [avi]

This video demonstrates the range of positions and orientations which can be detected by our system.

[ogg] [avi]

This video demonstrates that multiple people may be detected at the same time.

The GUI uses a generic model, based on the average face from the USF Human ID Morphable Faces Database.

Josiah Yoder, Henry Medeiros, Johnny Park, and Avinash C Kak, "Cluster-Based Distributed Face Tracking in Camera Networks", IEEE Transactions on Image Processing, vol. 19, no. 20, pp. 2551--2563, October 2010 [pdf]