2013 | OriginalPaper | Buchkapitel
Gait-Based Gender Classification Using Persistent Homology
verfasst von : Javier Lamar Leon, Andrea Cerri, Edel Garcia Reyes, Rocio Gonzalez Diaz
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer Berlin Heidelberg
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In this paper, a topological approach for gait-based gender recognition is presented. First, a stack of human silhouettes, extracted by background subtraction and thresholding, were glued through their gravity centers, forming a 3D digital image
I
. Second, different
filters
(i.e. particular orders of the simplices) are applied on ∂
K
(
I
) (a simplicial complex obtained from
I
) which capture relations among the parts of the human body when walking. Finally, a
topological signature
is extracted from the persistence diagram according to each filter. The measure cosine is used to give a similarity value between topological signatures. The novelty of the paper is a notion of robustness of the provided method (which is also valid for gait recognition). Three experiments are performed using all human-camera view angles provided in CASIA-B database. The first one evaluates the named topological signature obtaining 98.3% (lateral view) of correct classification rates, for gender identification. The second one shows results for different human-camera distances according to training and test (i.e. training with a human-camera distance and test with a different one). The third one shows that upper body is more discriminative than lower body.