Age-invariant face recognition system using combined shape and texture features

Abstract

This work presents an approach for combining texture and shape feature sets towards age-invariant face recognition. Physiological studies have proven that the human visual system can recognise familiar faces at different ages from the face outline alone. Based on this scientific fact, the phase congruency features for shape analysis were adopted to produce a face edge map. This was beneficial in tracking the craniofacial growth pattern for each subject. Craniofacial growth is common during childhood years, but after the age of 18, the texture variations start to show as the effect of facial aging. Therefore, in order to handle such texture variations, a variance of the well-known local binary pattern (LBP) texture descriptor, known as LBP variance was adopted. The results showed that fusing the shape and the texture features set yielded better performance than the individual performance of each feature set. Moreover, the individual verification accuracy for each feature set was improved when they were transformed to a kernel discriminative common vectors presentation. The system achieved an overall verification accuracy of above 93% when it was evaluated over the FG-NET face aging database.