REAL TIME AGE INVARIANT FACE RECOGNITION

Aging variation poses an interesting challenge for the task of automatic face
recognition. Most face recognition studies that have addressed this problem focused
on age estimation or aging simulation. Designing an appropriate feature
representation and an effective matching tramework for age invariant face recognition
remains an open problem. ln this research study, a novel age-invariant face
recognition framework that is built based on two face biometric traits is proposed. The
first trait is a set of anthropometric measurements acquired from the lip-nose
complex. Lip-nose complex measurements have been known in several physiological
studies to be discriminative among different ethnicities and among different genders
within the same ethnicity. The second trait is based on extracting features of the
periocular region using two robust descriptors, namely local binary pattems rotation
invariant descriptor (LBPV) and GIST descriptors.