Szczegóły publikacji
Opis bibliograficzny
The elastic $k$-nearest neighbours classifier for touch screen gestures / Krzysztof Rzecki, Leszek SIWIK, Mateusz Baran // W: Artificial Intelligence and Soft Computing : 18th international conference, ICAISC 2019 : Zakopane, June 16–20, 2019 : proceedings, Pt. 1 / eds. Leszek Rutkowski, [et al.]. — Cham : Springer Nature Switzerland AG, cop. 2019. — (Lecture Notes in Computer Science ; ISSN 0302-9743. Lecture Notes in Artificial Intelligence ; LNAI 11508). — ISBN: 978-3-030-20911-7; e-ISBN: 978-3-030-20912-4. — S. 608-615. — Bibliogr. s. 614-615, Abstr. — Publikacja dostępna online od: 2019-05-24
Autorzy (3)
- Rzecki Krzysztof
- AGHSiwik Leszek
- Baran Mateusz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 123346 |
|---|---|
| Data dodania do BaDAP | 2019-12-09 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-20912-4_55 |
| Rok publikacji | 2019 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Artificial Intelligence and Soft Computing 2019 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Touch screen gestures are a well-known method of person authentication in mobile devices. In most applications it is, however, reduced to checking if the user entered the correct pattern. Using additional information based on the speed and shape of finger movements can provide higher security without significantly impacting the convenience of this authorization method. In this work a new distance function for the k-nearest neighbour (kNN) classifier is considered in the problem of person recognition based on touch screen gestures. The function is based on the well-known $$\mathrm {L}^p$$ distance and the elastic distance considered in elastic shape analysis. Performance of the classifier is measured using 5-fold stratified cross-validation on a set of 12 people. Only four gesture performances per gesture for each person are used to train a model. The effects of sampling rate on the classifier performance is also measured. The kNN classifier with the proposed distance function has higher accuracy than both the $$\mathrm {L}^p$$ distance and the elastic distance. © 2019, Springer Nature Switzerland AG.
