Measuring face familiarity and its application to face recognition
The familiarity of faces is one of the key factors that come into play during human face analysis. However, there is very little research that studies face familiarity. In this paper, two methods are proposed to quantitatively measure the degree of familiarity of a face with respect to a known set. The methods are in accordance with the psychological study. In particular, non-negative matrix factorization (NMF) is extended to learn a localized non-overlapping subspace representation of commonly experienced facial patterns from known faces. The familiarity of a given face is then measured based on its reconstruction error after being projected into the learned extended NMF subspaces. A subjective study involving 50 subjects indicates the proposed familiarity measurement is in line with human judgments. Furthermore, the familiarity vector generated during the measuring process is employed for face recognition. Experiments based on the standard FERET evaluation protocol demonstrates the efficacy of the familiarity based representation for face recognition.