Common and Adapted Vocabularies for Face Verification

Abstract

Face verification in the presence of age progression is an important problem that has not been widely addressed. Despite appearance changes for same person due to aging, they are more similar compared to facial images from different individuals. Hence, we design common and adapted vocabularies, where common vocabulary describes contents of general population and adapted vocabulary represents specific characteristics of one of image facial pairs. And the other image is characterized with a concatenation histogram of common and adapted visual words counts, termed as “age-invariant distinctive representation”. The representation describes whether the image content is best modeled by the common vocabulary or the corresponding adapted vocabulary, which is further used to accomplish the face verification. The proposed approach is tested on the FGnet dataset and a collection of real-world facial images from identification card. The experimental results demonstrate the effectiveness of the proposed method for verification of identity at a modest computational cost.