Szczegóły publikacji
Opis bibliograficzny
Classification algorithm for person identification and gesture recognition based on hand gestures with small training sets / Krzysztof RZECKI // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220. — 2020 — vol. 20 iss. 24 art. no. 7279, s. 1–14. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 12–14, Abstr. — Publikacja dostępna online od: 2020-12-18
Autor
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 136205 |
|---|---|
| Data dodania do BaDAP | 2021-09-16 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s20247279 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
Classification algorithms require training data initially labelled by classes to build a model and then to be able to classify the new data. The amount and diversity of training data affect the classification quality and usually the larger the training set, the better the accuracy of classification. In many applications only small amounts of training data are available. This article presents a new time series classification algorithm for problems with small training sets. The algorithm was tested on hand gesture recordings in tasks of person identification and gesture recognition. The algorithm provides significantly better classification accuracy than other machine learning algorithms. For 22 different hand gestures performed by 10 people and the training set size equal to 5 gesture execution records per class, the error rate for the newly proposed algorithm is from 37% to 75% lower than for the other compared algorithms. When the training set consists of only one sample per class the new algorithm reaches from 45% to 95% lower error rate. Conducted experiments indicate that the algorithm outperforms state-of-the-art methods in terms of classification accuracy in the problem of person identification and gesture recognition.
