Open-Set Face Verification Algorithm Using Competitive Negative Samples

Abstract

A novel face verification algorithm using competitive negative samples is proposed. In the algorithm, the tested face matches not only with the claimed client face but also with competitive negative samples, and all the matching scores are combined to make a final decision. Based on the algorithm, three schemes, including closestnegative-sample scheme, all-negative-sample scheme, and closest-few-negative-sample scheme, are designed. They are tested and compared with the traditional similaritybased verification approach on several databases with different features and classifiers. Experiments demonstrate that the three schemes reduce the verification error rate by 25.15%, 30.24%, and 30.97%, on average, respectively.