We are developing solutions that will enhance the performance of face recognition engines by reducing accuracy errors associated with longitudinal aging. We have published articles on mitigating aging effects on commercial FR engines. (See pub lists.) We collaborate with FR developers and manufacturers to assist them in designing a more robust system to aging. Our long experience in this area has imparted lots of wisdom regarding feature selections and matching enhancements. We have extended the work for adult aging to that of children with support by researchers at Concordia University.
Most FR work has been evaluated on adult faces, and adult faces close in time (few days to few years between gallery and probe). We will soon begin a large collection effort focused on children and teenagers (pre-adults) to address the concerns of FR efficacy on this group. We hope to make this data collection available to the research community in the future.