Weighted group sparse representation based on robust regression for face recognition
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Combinational subsequence matching for human identification from general actions
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Superpixel-wise semi-supervised structural sparse coding classifier for image segmentation
Engineering Applications of Artificial Intelligence
Gait recognition based on shape and motion analysis of silhouette contours
Computer Vision and Image Understanding
Unsupervised images segmentation via incremental dictionary learning based sparse representation
Information Sciences: an International Journal
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In this paper, we propose a new patch distribution feature (PDF) (i.e., referred to as Gabor-PDF) for human gait recognition. We represent each gait energy image (GEI) as a set of local augmented Gabor features, which concatenate the Gabor features extracted from different scales and different orientations together with the X–Y coordinates. We learn a global Gaussian mixture model (GMM) (i.e., referred to as the universal background model) with the local augmented Gabor features from all the gallery GEIs; then, each gallery or probe GEI is further expressed as the normalized parameters of an image-specific GMM adapted from the global GMM. Observing that one video is naturally represented as a group of GEIs, we also propose a new classification method called locality-constrained group sparse representation (LGSR) to classify each probe video by minimizing the weighted $l_{1, 2}$ mixed-norm-regularized reconstruction error with respect to the gallery videos. In contrast to the standard group sparse representation method that is a special case of LGSR, the group sparsity and local smooth sparsity constraints are both enforced in LGSR. Our comprehensive experiments on the benchmark USF HumanID database demonstrate the effectiveness of the newly proposed feature Gabor-PDF and the new classification method LGSR for human gait recognition. Moreover, LGSR using the new feature Gabor-PDF achieves the best average Rank-1 and Rank-5 recognition rates on this database among all gait recognition algorithms proposed to date.