On the Use of Discriminative Cohort Score Normalization for Unconstrained Face Recognition

Facial imaging has been largely addressed for automatic personal identification, in a variety of different environments. However, automatic face recognition becomes very challenging whenever the acquisition conditions are unconstrained. In this paper, a picture-specific cohort normalization approach, based on polynomial regression, is proposed to enhance the robustness of face matching under challenging conditions. A careful analysis is presented to better understand the actual discriminative power of a given cohort set. In particular, it is shown that the cohort polynomial regression alone conveys some discriminative information on the matching face pair, which is just marginally worse than the raw matching score. The influence of the cohort set size in the matching accuracy is also investigated. Further, tests performed on the Face Recognition Grand Challenge ver 2 database and the labeled faces in the wild database allowed to determine the relation between the quality of the cohort samples and cohort normalization performance. Experimental results obtained from the LFW data set demonstrate the effectiveness of the proposed approach to improve the recognition accuracy in unconstrained face acquisition scenarios.