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Occlusion is a common yet challenging problem in face recognition. Most of the existing approaches cannot achieve the accuracy of the recognition with high efficiency in the occlusion case. To address this problem, this paper proposes a novel algorithm, called efficient locality-constrained occlusion coding (ELOC), improving the previous sparse error correction with Markov random fields (SEC_MRF) algorithm. The proposed approach estimates and excludes occluded region by locality-constrained linear coding (LLC), which avoids the time-consuming l 1 -minimization and exhaustive subject-by-subject search during the occlusion estimation, and greatly reduces the running time of recognition. Moreover, by simplifying the regularization, the ELOC can be further accelerated. Experimental results on several face databases show that our algorithms significantly improve the previous algorithms in efficiency without losing too much accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new online learning method for measuring affinity between tracklets in multi-target tracking. As targets and background usually keep changing in the video, fixed affinity measurement could not adapt to their variations. Most existing affinity learning methods construct labeled samples based on the obtained tracklets, and then minimize a predefined loss function to get an optimal affinity measurement. However, those methods simply assume that the training error equals to testing error which is not true in many of real time tracking scenarios. Differently, we propose to learn affinity measurement through CovBoosting, which considers the evolution of the tracklets and could obtain affinity measurement with more discriminative ability. To deal with targets׳ disappearance and new targets׳ appearance, we combine tracklet affinity with contextual information to do an optimal inference. Moreover, an online updating algorithm is developed to guarantee that the learned tracklet affinity is always optimal for tracking targets in current sliding window. Experimental results on benchmark datasets demonstrate that tracklet affinity learned with our method is more discriminative and could greatly improve the performance of the multi-target tracker. [ABSTRACT FROM AUTHOR]