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Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed...
To facilitate human-robot interactions, human gender information is very important. Motivated by the success of manifold learning for visual recognition, we present a novel clustering-based discriminative locality alignment (CDLA) algorithm to discover the low-dimensional intrinsic submanifold from the embedding high-dimensional ambient space for improving the face gender recognition performance....
Dimension reduction algorithms have attracted a lot of attentions in face recognition and human gait recognition because they can select a subset of effective and efficient discriminative features. In this paper, we apply the discriminative geometry preserving projections (DGPP), a new subspace learning algorithm to address these problems. DGPP models both the intraclass geometry and interclass discrimination...
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