- Tytuł:
- Optimized jk-nearest neighbor based online signature verification and evaluation of main parameters
- Autorzy:
-
Saleem, Muhammad
Kovari, Bence - Powiązania:
- https://bibliotekanauki.pl/articles/2097967.pdf
- Data publikacji:
- 2021
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
k-nearest neighbor
online signature verification
classification - Opis:
- In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) algorithm for online signature verification. The effect of its main parameters is evaluated and used to build an optimized system. The results show that the jk-NN classifier improves the verification accuracy by 0.73–10% as compared to a traditional one-class k-NN classifier. The algorithm achieved reasonable accuracy for different databases: a 3.93% average error rate when using the SVC2004, 2.6% for the MCYT-100, 1.75% for the SigComp’11, and 6% for the SigComp’15 databases. These results followed a state-of-the-art accuracy evaluation where both forged and genuine signatures were used in the training phase. Another scenario is also presented in this paper by using an optimized jk-NN algorithm that uses specifically chosen parameters and a procedure to pick the optimal value for k using only the signer’s reference signatures to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error rates that were achieved were 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp’15, and 2.22% for SigComp’11.
- Źródło:
-
Computer Science; 2021, 22 (4); 539--551
1508-2806
2300-7036 - Pojawia się w:
- Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł