Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods
Gait is considered a valuable biometric feature, and it is essential for uncovering the latent information embedded within gait patterns. Gait recognition methods are expected to serve as significant components in numerous applications. However, existing gait recognition methods exhibit limitations...
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3794 |
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| author | Ruixiang Kan Mei Wang Tian Luo Hongbing Qiu |
| author_facet | Ruixiang Kan Mei Wang Tian Luo Hongbing Qiu |
| author_sort | Ruixiang Kan |
| collection | DOAJ |
| description | Gait is considered a valuable biometric feature, and it is essential for uncovering the latent information embedded within gait patterns. Gait recognition methods are expected to serve as significant components in numerous applications. However, existing gait recognition methods exhibit limitations in complex scenarios. To address these, we construct a dual-Kinect V2 system that focuses more on gait skeleton joint data and related acoustic signals. This setup lays a solid foundation for subsequent methods and updating strategies. The core framework consists of enhanced ensemble learning methods and Dempster–Shafer Evidence Theory (D-SET). Our recognition methods serve as the foundation, and the decision support mechanism is used to evaluate the compatibility of various modules within our system. On this basis, our main contributions are as follows: (1) an improved gait skeleton joint AdaBoost recognition method based on Circle Chaotic Mapping and Gramian Angular Field (GAF) representations; (2) a data-adaptive gait-related acoustic signal AdaBoost recognition method based on GAF and a Parallel Convolutional Neural Network (PCNN); and (3) an amalgamation of the Triangulation Topology Aggregation Optimizer (TTAO) and D-SET, providing a robust and innovative decision support mechanism. These collaborations improve the overall recognition accuracy and demonstrate their considerable application values. |
| format | Article |
| id | doaj-art-cfb5b3dac4ec46e482ce3381fe02c69f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-cfb5b3dac4ec46e482ce3381fe02c69f2025-08-20T03:29:52ZengMDPI AGSensors1424-82202025-06-012512379410.3390/s25123794Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support MethodsRuixiang Kan0Mei Wang1Tian Luo2Hongbing Qiu3School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaCollege of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaSchool of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, ChinaGait is considered a valuable biometric feature, and it is essential for uncovering the latent information embedded within gait patterns. Gait recognition methods are expected to serve as significant components in numerous applications. However, existing gait recognition methods exhibit limitations in complex scenarios. To address these, we construct a dual-Kinect V2 system that focuses more on gait skeleton joint data and related acoustic signals. This setup lays a solid foundation for subsequent methods and updating strategies. The core framework consists of enhanced ensemble learning methods and Dempster–Shafer Evidence Theory (D-SET). Our recognition methods serve as the foundation, and the decision support mechanism is used to evaluate the compatibility of various modules within our system. On this basis, our main contributions are as follows: (1) an improved gait skeleton joint AdaBoost recognition method based on Circle Chaotic Mapping and Gramian Angular Field (GAF) representations; (2) a data-adaptive gait-related acoustic signal AdaBoost recognition method based on GAF and a Parallel Convolutional Neural Network (PCNN); and (3) an amalgamation of the Triangulation Topology Aggregation Optimizer (TTAO) and D-SET, providing a robust and innovative decision support mechanism. These collaborations improve the overall recognition accuracy and demonstrate their considerable application values.https://www.mdpi.com/1424-8220/25/12/3794gait recognitionmulti-sensor systemvisual–audio informationensemble learningGramian Angular FieldsDempster–Shafer Evidence Theory |
| spellingShingle | Ruixiang Kan Mei Wang Tian Luo Hongbing Qiu Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods Sensors gait recognition multi-sensor system visual–audio information ensemble learning Gramian Angular Fields Dempster–Shafer Evidence Theory |
| title | Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods |
| title_full | Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods |
| title_fullStr | Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods |
| title_full_unstemmed | Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods |
| title_short | Gait Recognition via Enhanced Visual–Audio Ensemble Learning with Decision Support Methods |
| title_sort | gait recognition via enhanced visual audio ensemble learning with decision support methods |
| topic | gait recognition multi-sensor system visual–audio information ensemble learning Gramian Angular Fields Dempster–Shafer Evidence Theory |
| url | https://www.mdpi.com/1424-8220/25/12/3794 |
| work_keys_str_mv | AT ruixiangkan gaitrecognitionviaenhancedvisualaudioensemblelearningwithdecisionsupportmethods AT meiwang gaitrecognitionviaenhancedvisualaudioensemblelearningwithdecisionsupportmethods AT tianluo gaitrecognitionviaenhancedvisualaudioensemblelearningwithdecisionsupportmethods AT hongbingqiu gaitrecognitionviaenhancedvisualaudioensemblelearningwithdecisionsupportmethods |