Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification
This work addresses the problem of matching pedestrian images across cameras for surveillance applications, but it is incremental as it builds on existing metric learning techniques.
The paper tackles person re-identification by proposing a metric learning framework using Kernel Fisher Discriminant Analysis to maximize inter-class and minimize intra-class variance, achieving competitive performance with state-of-the-art methods on three benchmark datasets.
Person re-identification addresses the problem of matching pedestrian images across disjoint camera views. Design of feature descriptor and distance metric learning are the two fundamental tasks in person re-identification. In this paper, we propose a metric learning framework for person re-identification, where the discriminative metric space is learned using Kernel Fisher Discriminant Analysis (KFDA), to simultaneously maximize the inter-class variance as well as minimize the intra-class variance. We derive a Mahalanobis metric induced by KFDA and argue that KFDA is efficient to be applied for metric learning in person re-identification. We also show how the efficiency of KFDA in metric learning can be further enhanced for person re-identification by using two simple yet efficient multiple kernel learning methods. We conduct extensive experiments on three benchmark datasets for person re-identification and demonstrate that the proposed approaches have competitive performance with state-of-the-art methods.