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Kernel multiblock partial least squares for a scalable and multicamera person reidentification system

Abstract. Person reidentification (Re-ID) aims at establishing global identities for individuals as they move across a camera network. It is a challenging task due to the drastic appearance changes that occur between cameras as a consequence of different pose and illumination conditions. Pairwise matching models yield state-of-the-art results in most of the person Re-ID datasets by capturing nuances that are robust and discriminative for a specific pair of cameras. Nonetheless, pairwise models are not scalable with the number of surveillance cameras. Therefore, elegant solutions combining scalability with high matching rates are crucial for the person Re-ID in real-world scenarios. We tackle this problem proposing a multicamera nonlinear regression model called kernel multiblock partial least squares (kernel MBPLS), a single subspace model for the entire camera network that uses all the labeled information. In this subspace, probe and gallery individual can be successfully matched. Experimental results in three multicamera person Re-ID datasets (WARD, RAiD, and SAIVT-SoftBIO) demonstrate that the kernel MBPLS presents favorable aspects, such as the scalability and robustness with respect to the number of cameras combined with the high matching rates.