The palmprint recognition has become a focus in biological recognition and image processing fields. In this process, the features extraction (with particular attention to palmprint principal line extraction) is especially important. Although a lot of work has been reported, the representation of palmprint is still an open issue. In this paper we propose a simple, efficient, and accurate palmprint principal lines extraction method. Our approach consists of six simple steps: normalization, median filtering, average filters along four prefixed directions, grayscale bottom-hat filtering, combination of bottom-hat filtering, binarization and post processing. The contribution of our work is a new method for palmprint principal lines detection and a new dataset of hand labeled principal lines images (that we use as ground truth in the experiments). Preliminary experimental results showed good performance in terms of accuracy with respect to three methods of the state of the art.
Bruno, A., Carminetti, P., Gentile, V., LA CASCIA, M., & Mancino, E. (2014). Palmprint principal lines extraction. In BIOMS 2014 - 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, Proceedings 6951535 (pp. 50-56).
Data di pubblicazione: | 2014 |
Titolo: | Palmprint principal lines extraction |
Autori: | |
Citazione: | Bruno, A., Carminetti, P., Gentile, V., LA CASCIA, M., & Mancino, E. (2014). Palmprint principal lines extraction. In BIOMS 2014 - 2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, Proceedings 6951535 (pp. 50-56). |
Abstract: | The palmprint recognition has become a focus in biological recognition and image processing fields. In this process, the features extraction (with particular attention to palmprint principal line extraction) is especially important. Although a lot of work has been reported, the representation of palmprint is still an open issue. In this paper we propose a simple, efficient, and accurate palmprint principal lines extraction method. Our approach consists of six simple steps: normalization, median filtering, average filters along four prefixed directions, grayscale bottom-hat filtering, combination of bottom-hat filtering, binarization and post processing. The contribution of our work is a new method for palmprint principal lines detection and a new dataset of hand labeled principal lines images (that we use as ground truth in the experiments). Preliminary experimental results showed good performance in terms of accuracy with respect to three methods of the state of the art. |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/BIOMS.2014.6951535 |
Settore Scientifico Disciplinare: | Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni |
Appare nelle tipologie: | 2.01 Capitolo o Saggio |
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