Infra-red pupil detection for use in a face recognition system

This paper presents a new method of eye localisation and face segmentation for use in a face recognition system. By using two near infrared light sources, we have shown that the face can be coarsely segmented, and the eyes can be accurately located, increasing the accuracy of the face localisation and improving the overall speed of the system. The system is able to locate both eyes within 25% of the eye-to-eye distance in over 96% of test cases.

Adaptive optical flow for person tracking

Person tracking systems are dependent on being able to locate a person accurately across a series of frames. Optical flow can be used to segment a moving object from a scene, provided the expected velocity of the moving object is known; but successful detection also relies on being able segment the background. A problem with existing optical flow techniques is that they don’t discriminate the foreground from the background, and so often detect motion (and thus the object) in the background. To overcome this problem, we propose a new optical flow technique, that is based upon an adaptive background segmentation technique, which only determines optical flow in regions of motion. This technique has been developed with a view to being used in surveillance systems, and our testing shows that for this application it is more effective than other standard optical flow techniques.