Improved gait recognition by multiple-projections normalization |
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Authors: | Murat Ekinci Murat Aykut |
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Affiliation: | 1. Computer Vision Lab., Department of Computer Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey
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Abstract: | Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques
are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion
object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance
vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier
Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette
sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation
is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification
technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively
constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first
approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated
to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition.
The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait
databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing
amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed
algorithm is comparatively illustrated to take into consideration the published gait recognition approaches. |
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