Using empirical mode decomposition and Kullback-Leibler distance for online handwritten signature verification

Autor:	Toufik Hafs, Amine Nait-Ali, Mohamed Boughazi, Layachi Bennacer, Hanene Brahmia
Rok vydání:	2019
Předmět:	Kullback–Leibler divergence Similarity (geometry) Biometrics Computer science business.industry Pattern recognition General Medicine Hilbert–Huang transform Signature (logic) symbols.namesake symbols Preprocessor Hilbert transform Artificial intelligence business Reliability (statistics)
Zdroj:	International Journal of Applied Pattern Recognition. 6:15
ISSN:	2049-8888 2049-887X
DOI:	10.1504/ijapr.2019.10026037
Popis:	The signature is one of the most accepted biometric modalities in the world. In this paper, we present a new method for online handwritten signature based on the empirical mode decomposition (EMD). After extracting each of the signature coordinates, a phase of preprocessing and normalisation is carried out. Then, the features of the signatures are extracted by using the EMD. After that, three similarity measures are used to match the signatures between them. The database used in our work is the one used in SVC (2004). Experimental results confirm the effectiveness of our approach and show the level of its reliability. Finally, the proposed method gives an EER of 2.03% and allows high rates of recognition compared to other approaches.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2fcfe82435e68ebef872f3fba7c6f87a https://doi.org/10.1504/ijapr.2019.10026037 Zobrazit plný text záznamu