Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387435414> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W4387435414 endingPage "2744" @default.
- W4387435414 startingPage "2727" @default.
- W4387435414 abstract "Biometric gait recognition is a lesser-known but emerging and effective biometric recognition method which enables subjects’ walking patterns to be recognized. Existing research in this area has primarily focused on feature analysis through the extraction of individual features, which captures most of the information but fails to capture subtle variations in gait dynamics. Therefore, a novel feature taxonomy and an approach for deriving a relationship between a function of one set of gait features with another set are introduced. The gait features extracted from body halves divided by anatomical planes on vertical, horizontal, and diagonal axes are grouped to form canonical gait covariates. Canonical Correlation Analysis is utilized to measure the strength of association between the canonical covariates of gait. Thus, gait assessment and identification are enhanced when more semantic information is available through CCA-based multi-feature fusion. Hence, Carnegie Mellon University’s 3D gait database, which contains 32 gait samples taken at different paces, is utilized in analyzing gait characteristics. The performance of Linear Discriminant Analysis, K-Nearest Neighbors, Naive Bayes, Artificial Neural Networks, and Support Vector Machines was improved by a 4% average when the CCA-utilized gait identification approach was used. A significant maximum accuracy rate of 97.8% was achieved through CCA-based gait identification. Beyond that, the rate of false identifications and unrecognized gaits went down to half, demonstrating state-of-the-art for gait identification." @default.
- W4387435414 created "2023-10-09" @default.
- W4387435414 creator A5015402013 @default.
- W4387435414 creator A5065102655 @default.
- W4387435414 date "2023-01-01" @default.
- W4387435414 modified "2023-10-09" @default.
- W4387435414 title "3-D Gait Identification Utilizing Latent Canonical Covariates Consisting of Gait Features" @default.
- W4387435414 doi "https://doi.org/10.32604/cmc.2023.032069" @default.
- W4387435414 hasPublicationYear "2023" @default.
- W4387435414 type Work @default.
- W4387435414 citedByCount "0" @default.
- W4387435414 crossrefType "journal-article" @default.
- W4387435414 hasAuthorship W4387435414A5015402013 @default.
- W4387435414 hasAuthorship W4387435414A5065102655 @default.
- W4387435414 hasBestOaLocation W43874354141 @default.
- W4387435414 hasConcept C116834253 @default.
- W4387435414 hasConcept C119043178 @default.
- W4387435414 hasConcept C119857082 @default.
- W4387435414 hasConcept C138885662 @default.
- W4387435414 hasConcept C151800584 @default.
- W4387435414 hasConcept C153180895 @default.
- W4387435414 hasConcept C153874254 @default.
- W4387435414 hasConcept C154945302 @default.
- W4387435414 hasConcept C173906292 @default.
- W4387435414 hasConcept C177264268 @default.
- W4387435414 hasConcept C184297639 @default.
- W4387435414 hasConcept C199360897 @default.
- W4387435414 hasConcept C2776401178 @default.
- W4387435414 hasConcept C41008148 @default.
- W4387435414 hasConcept C41895202 @default.
- W4387435414 hasConcept C52622490 @default.
- W4387435414 hasConcept C59822182 @default.
- W4387435414 hasConcept C69738355 @default.
- W4387435414 hasConcept C71924100 @default.
- W4387435414 hasConcept C86803240 @default.
- W4387435414 hasConcept C99508421 @default.
- W4387435414 hasConceptScore W4387435414C116834253 @default.
- W4387435414 hasConceptScore W4387435414C119043178 @default.
- W4387435414 hasConceptScore W4387435414C119857082 @default.
- W4387435414 hasConceptScore W4387435414C138885662 @default.
- W4387435414 hasConceptScore W4387435414C151800584 @default.
- W4387435414 hasConceptScore W4387435414C153180895 @default.
- W4387435414 hasConceptScore W4387435414C153874254 @default.
- W4387435414 hasConceptScore W4387435414C154945302 @default.
- W4387435414 hasConceptScore W4387435414C173906292 @default.
- W4387435414 hasConceptScore W4387435414C177264268 @default.
- W4387435414 hasConceptScore W4387435414C184297639 @default.
- W4387435414 hasConceptScore W4387435414C199360897 @default.
- W4387435414 hasConceptScore W4387435414C2776401178 @default.
- W4387435414 hasConceptScore W4387435414C41008148 @default.
- W4387435414 hasConceptScore W4387435414C41895202 @default.
- W4387435414 hasConceptScore W4387435414C52622490 @default.
- W4387435414 hasConceptScore W4387435414C59822182 @default.
- W4387435414 hasConceptScore W4387435414C69738355 @default.
- W4387435414 hasConceptScore W4387435414C71924100 @default.
- W4387435414 hasConceptScore W4387435414C86803240 @default.
- W4387435414 hasConceptScore W4387435414C99508421 @default.
- W4387435414 hasIssue "3" @default.
- W4387435414 hasLocation W43874354141 @default.
- W4387435414 hasOpenAccess W4387435414 @default.
- W4387435414 hasPrimaryLocation W43874354141 @default.
- W4387435414 hasRelatedWork W1565185441 @default.
- W4387435414 hasRelatedWork W2039206722 @default.
- W4387435414 hasRelatedWork W2096089271 @default.
- W4387435414 hasRelatedWork W2354323478 @default.
- W4387435414 hasRelatedWork W2356150353 @default.
- W4387435414 hasRelatedWork W2511384863 @default.
- W4387435414 hasRelatedWork W2918515951 @default.
- W4387435414 hasRelatedWork W2923628599 @default.
- W4387435414 hasRelatedWork W2985746494 @default.
- W4387435414 hasRelatedWork W4206042385 @default.
- W4387435414 hasVolume "76" @default.
- W4387435414 isParatext "false" @default.
- W4387435414 isRetracted "false" @default.
- W4387435414 workType "article" @default.