2009 |
Štruc, Vitomir; Pavešić, Nikola Gaussianization of image patches for efficient palmprint recognition Journal Article In: Electrotechnical Review, vol. 76, no. 5, pp. 245-250, 2009. Abstract | Links | BibTeX | Tags: biometrics, gaussianization, histogram remapping, palmprint recognition, palmprints, preprocessing @article{EV_2009_palms,In this paper we present a comparison of the two dominant image preprocessing techniques for palmprint recognition, namely, histogram equalization and mean-variance normalization. We show that both techniques pursue a similar goal and that the difference in recognition efficiency stems from the fact that not all assumptions underlying the mean-variance normalization approach are always met. We present an alternative justification of why histogram equalization ensures enhanced verification performance, and, based on the findings, propose two novel preprocessing techniques: gaussianization of the palmprint images and gaussianization of image patches. We present comparative results obtained on the PolyU database and show that the patch-based normalization technique ensures stat-of-the-art recognition results with a simple feature extraction method and the nearest neighbor classifier. |
2008 |
Štruc, Vitomir; Pavešić, Nikola A palmprint verification system based on phase congruency features Proceedings Article In: Schouten, Ben; Juul, Niels Christian; Drygajlo, Andrzej; Tistarelli, Massimo (Ed.): Biometrics and Identity Management, pp. 110-119, Springer-Verlag, Berlin, Heidelberg, 2008. Abstract | Links | BibTeX | Tags: feature extraction, palmprint recognition, palmprint verification, palmprints, performance evaluation @inproceedings{BioID2008,The paper presents a fully automatic palmprint verification system which uses 2D phase congruency to extract line features from a palmprint image and subsequently performs linear discriminant analysis on the computed line features to represent them in a more compact manner. The system was trained and tested on a database of 200 people (2000 hand images) and achieved a false acceptance rate (FAR) of 0.26% and a false rejection rate (FRR) of 1.39% in the best performing verification experiment. In a comparison, where in addition to the proposed system, three popular palmprint recognition techniques were tested for their verification accuracy, the proposed system performed the best. |