Human Gait Recognition Using Bag of Words Feature Representation Method
This work addresses gait recognition for biometric identification, but it is incremental as it applies an existing bag-of-words method to a new domain with specific data.
The paper tackled human gait recognition by proposing a bag-of-words feature representation method, achieving significant accuracy improvements over common statistical features across all classifiers tested on a dataset of 93 individuals.
In this paper, we propose a novel gait recognition method based on a bag-of-words feature representation method. The algorithm is trained, tested and evaluated on a unique human gait data consisting of 93 individuals who walked with comfortable pace between two end points during two different sessions. To evaluate the effectiveness of the proposed model, the results are compared with the outputs of the classification using extracted features. As it is presented, the proposed method results in significant improvement accuracy compared to using common statistical features, in all the used classifiers.