A High-Performance Gait Recognition Method Based on n-Fold Bernoulli Theory

Zhou, Qing; Rasol, Jarhinbek; Xu, Yansheng; Zhang, Zhaoxiang; Hu, Lujuan

doi:10.1109/access.2022.3212366

Cited by 3 publications

(3 citation statements)

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“…Despite all of that, the obtained results were very good, and it is one of the best results when compared to those available in literature. A slightly higher level of recognition, that of 99.8%, had been obtained by [ 33 ]. However, the authors of those tests conducted them on a sample group of only 50 people which, as has been shown by [ 43 ], significantly impacts the results obtained.…”

Section: Discussionmentioning

confidence: 79%

“…Equally high recognition rates have been presented in [ 36 ], where a method called Gait-ViT allowed the accurate recognition of 99.51 to 100 percent of gait cycles. In the work of [ 36 ], in contrast to [ 33 ], very good results were also gained for a database containing a larger number of people (OU-LP data set, 3916 participants). It should be noted that the difficulty in the direct comparison of results lies in the fact that in both of the above-mentioned works, images from a video camera were used as data describing human gait.…”

Section: Discussionmentioning

confidence: 99%

“…Very good results were also obtained using other methods. In [ 33 ], Kinect was employed to acquire three-dimensional coordinates of human bones. Distances between bone nodes became the features, while the classifier was a support vector machine (SVM) using one-versus-one and one-versus-all algorithms to solve the multiclassification task.…”

Section: Related Workmentioning

confidence: 99%

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Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Derlatka

Borowska

2023

Sensors

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Human gait recognition is one of the most interesting issues within the subject of behavioral biometrics. The most significant problems connected with the practical application of biometric systems include their accuracy as well as the speed at which they operate, understood both as the time needed to recognize a particular person as well as the time necessary to create and train a biometric system. The present study made use of an ensemble of heterogeneous base classifiers to address these issues. A Heterogeneous ensemble is a group of classification models trained using various algorithms and combined to output an effective recognition A group of parameters identified on the basis of ground reaction forces was accepted as input signals. The proposed solution was tested on a sample of 322 people (5980 gait cycles). Results concerning the accuracy of recognition (meaning the Correct Classification Rate quality at 99.65%), as well as operation time (meaning the time of model construction at <12.5 min and the time needed to recognize a person at <0.1 s), should be considered as very good and exceed in quality other methods so far described in the literature.

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Section: Discussionmentioning

confidence: 79%