Kernel Hierarchical PCA for person re-identification
Person re-identification (Re-ID) maintains a global identity for an individual while he moves along a large area covered by multiple cameras. Re-ID enables a multi-camera monitoring of individual activity that is critical for surveillance systems. However, the low-resolution images combined with the different poses, illumination conditions and camera viewpoints make person Re-ID a challenging problem. To reach a higher matching performance, state-of-the-art methods map the data to a nonlinear feature space where they learn a cross-view matching function using training data. Kernel PCA is a statistical method that learns a common subspace that captures most of the variability of samples using a small number of vector basis. However, Kernel PCA disregards that images were captured by distinct cameras, a critical problem in person Re-ID. Differently, Hierarchical PCA (HPCA) captures a consensus projection between multiblock data (e.g, two camera views), but it is a linear model. Therefore, we propose the Kernel Hierarchical PCA (Kernel HPCA) to tackle camera transition and dimensionality reduction in a unique framework. To the best of our knowledge, this is the first work to propose a kernel extension to the multiblock HPCA method. Experimental results demonstrate that Kernel HPCA reaches a matching performance comparable with state-of-the-art nonlinear subspace learning methods at PRID450S and VIPeR datasets. Furthermore, Kernel HPCA reaches a better combination of subspace learning and dimensionality requiring significantly lower subspace dimensions.