High Speed Image Searching For Human Gait Feature Selection

In this paper, a new patch distribution feature (i.e., referred to as Gabor-PDF) is used 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. A global Gaussian mixture model (GMM) (i.e., referred to as the universal back- ground 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, also a new classification method called locality-constrained group sparse representation (LGSR) to classify each probe video by minimizing the weighted l1,2 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. The same LGSR algorithm is used for both color images and for content based image retrieval (CBIR

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