Logit-RankBoost with pruning for face recognition

In this paper a novel ranking-based face recognition (FR) scheme is proposed. Compared with classical two-class (intra/extra person) and multi-class (each person a single class) schemes, the ranking-based method only takes into account the most relevant information in training data to find a solution, and therefore is more consistent with the objective of FR. In our approach, given a feature set and its similarity measure, all interested image pairs will be ordered by similarity. The solution to FR then becomes to explore a ranking function that can rank each intra-personal similarity prior to its relevant extra-personal similarities, which can be readily solved by rank boost algorithm. Furthermore in this paper, a logit-rank boost algorithm is proposed which can achieve better recognition performance, and a pruning technique is adopted to deal with the large amount of data that results in further improvement in recognition accuracy. Extensive experimental results on a consumer image collection and the FERET dataset are reported to show the effectiveness of our approach.