@inproceedings{Prates2016ICPR,

title = {Kernel Hierarchical PCA for Person Re-Identification},

author = {Raphael Felipe Carvalho de Prates and William Robson Schwartz},

url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/Kernel_HPCA_final.pdf},

year  = {2016},

date = {2016-12-13},

booktitle = {IAPR International Conference on Pattern Recognition (ICPR)},

abstract = {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 ReID. 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.},

keywords = {Kernel Hierarchical PCA, Kernel Partial Least Squares, Partial Least Squares, Person Re-Identification},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Prates2016AVSS,

title = {Kernel Partial Least Squares for Person Re-Identification},

author = {Raphael Felipe Carvalho de Prates and Marina Santos Oliveira and William Robson Schwartz},

url = {http://smartsenselab.dcc.ufmg.br/wp-content/uploads/2019/02/egpaper_for_DoubleBlindReview.pdf},

year  = {2016},

date = {2016-09-25},

booktitle = {IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS)},

abstract = {Person re-identification (Re-ID) keeps the same identity for a person as he moves along an area with nonoverlapping surveillance cameras. Re-ID is a challenging task due to appearance changes caused by different camera viewpoints, occlusion and illumination conditions. While robust and discriminative descriptors are obtained combining texture, shape and color features in a high-dimensional representation, the achievement of accuracy and efficiency demands dimensionality reduction methods. At this paper, we propose variations of Kernel Partial Least Squares (KPLS) that simultaneously reduce the dimensionality and increase the discriminative power. The Cross-View KPLS (X-KPLS) and KPLS Mode A capture cross-view discriminative information and are successful for unsupervised and supervised Re-ID. Experimental results demonstrate that XKPLS presents equal or higher matching results when compared to other methods in literature at PRID450S.},

keywords = {DeepEyes, Featured Publication, GigaFrames, HAR-HEALTH, Kernel Partial Least Squares, Kernel Partial Least Squares for Person Re-Identification, Person Re-Identification},

pubstate = {published},

tppubtype = {inproceedings}

}