A Grassmann graph embedding framework for gait analysis
نویسندگان
چکیده
منابع مشابه
A Grassmann graph embedding framework for gait analysis
Gait recognition is important in a wide range of monitoring and surveillance applications. Gait information has often been used as evidence when other biometrics is indiscernible in the surveillance footage. Building on recent advances of the subspace-based approaches, we consider the problem of gait recognition on the Grassmann manifold. We show that by embedding the manifold into reproducing ...
متن کاملMILE: A Multi-Level Framework for Scalable Graph Embedding
Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework – a generic methodology allowing contemporary graph embedding methods to scale to larg...
متن کاملA Grassmann framework for 4D facial shape analysis
In this paper, we investigate the contribution of dynamic evolution of 3D faces to identity recognition. To this end, we adopt a subspace representation of the flow of curvature-maps computed on 3D facial frames of a sequence, after normalizing their pose. Such representation allows us to embody the shape as well as its temporal evolution within the same subspace representation. Dictionary lear...
متن کاملOn the nucleus of the Grassmann embedding
Let n ≥ 3 and let F be a field of characteristic 2. Let DSp(2n, F) denote the dual polar space associated with the building of Type Cn over F and let Gn−2 denote the (n − 2)-Grassmannian of type Cn. Using the bijective correspondence between the points of Gn−2 and the quads of DSp(2n, F), we construct a full projective embedding of Gn−2 into the nucleus of the Grassmann embedding of DSp(2n, F)....
متن کاملMob { A Parallel Heuristic for Graph - Embedding
We have extended the Mob heuristic for graph partitioning [21] to grid and hypercube embedding and have e ciently implemented our new heuristic on the CM-2 Connection Machine. We have conducted an extensive series of experiments to show that it exploits parallelism, is fast, and gives very low embedding costs. For example, on the 32K-processor CM-2 it runs in less than 30 minutes on random grap...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: EURASIP Journal on Advances in Signal Processing
سال: 2014
ISSN: 1687-6180
DOI: 10.1186/1687-6180-2014-15