Multi‐perspective gait recognition based on classifier fusion

Wang, Xiuhui; Feng, Shaochong

doi:10.1049/iet-ipr.2018.6566

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…One flaw of this approach is the need for many data as well as significant calculating power. The other approach is the application of a group of simple classifiers which share in determining the classifying decision [ 19 , 20 , 21 ]. The contrast with deep learning classifier ensembles, in general, consists of simple classifiers with less enhanced algorithms.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Derlatka

Borowska

2023

Sensors

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“…Wang and Yan [21] proposed a cross-view gait recognition framework, which was based on ensemble learning for combining several gait learners together. Wang et al [22] presented a new algorithm for gait feature extraction, which deceptively extracts the dynamic gait characteristics of walking persons. These studies demonstrate the versatility and effectiveness of gait recognition technology based on deep learning.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Re-Identification Based on Gait Analysis

Shan,

Yu,

Gao

2023

IEEE Access

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Pedestrian re-identification is a crucial task in various safety applications, such as traffic management, collision avoidance, and emergency response. One of the challenging issues in pedestrian reidentification is how to identify the same object when the collection perspective changes or carrying different items. To address the above issues, this paper proposes a new gait based pedestrian re-identification method from the perspective of improving the representation ability of different pedestrians. The proposed method extracts spatial and temporal features separately from the gait data and designs a spatiotemporal feature fusion module at the top of the network to prevent features from interfering with each other in different domains. The temporal scoring strategy based on gated recurrent unit (GRU) is then used to evaluate the image quality of each frame in the network branch where temporal gait features are extracted. Finally, the proposed method is evaluated on two open datasets, CASIA-B and OU-ISIR-LP. Experimental results show that, compared with existing methods, the proposed method has a higher recognition rate under cross-view and scene conditions, such as carrying a bag and heavily clothed, in which the proposed method demonstrates better performance in pedestrian recognition. These results highlight the effectiveness and robustness of the proposed method and demonstrate its potential as a future research direction in pedestrian re-identification.INDEX TERMS Gait analysis, gait recognition, spatiotemporal feature fusion, pedestrian re-identification

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“…Gait recognition [7,8] was also used by S. M. Darwish, X. Wang, and S. Feng as a biometric trait for human identification and recognition. Fingerprint recognition [9] with the Support Vector Machine (SVM) classifier was used by M. Komeili, N. Armanfard, and D. Hatzinakos as another biometric trait.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

2020

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In the proposed study, we examined a multimodal biometric system having the utmost capability against spoof attacks. An enhanced anti-spoof capability is successfully demonstrated by choosing hand-related intrinsic modalities. In the proposed system, pulse response, hand geometry, and finger–vein biometrics are the three modalities of focus. The three modalities are combined using a fuzzy rule-based system that provides an accuracy of 92% on near-infrared (NIR) images. Besides that, we propose a new NIR hand images dataset containing a total of 111,000 images. In this research, hand geometry is treated as an intrinsic biometric modality by employing near-infrared imaging for human hands to locate the interphalangeal joints of human fingers. The L2 norm is calculated using the centroid of four pixel clusters obtained from the finger joint locations. This method produced an accuracy of 86% on the new NIR image dataset. We also propose finger–vein biometric identification using convolutional neural networks (CNNs). The CNN provided 90% accuracy on the new NIR image dataset. Moreover, we propose a robust system known as the pulse response biometric against spoof attacks involving fake or artificial human hands. The pulse response system identifies a live human body by applying a specific frequency pulse on the human hand. About 99% of the frequency response samples obtained from the human and non-human subjects were correctly classified by the pulse response biometric. Finally, we propose to combine all three modalities using the fuzzy inference system on the confidence score level, yielding 92% accuracy on the new near-infrared hand images dataset.

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Multi‐perspective gait recognition based on classifier fusion

Cited by 8 publications

References 20 publications

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition

Pedestrian Re-Identification Based on Gait Analysis

Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

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