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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Palmprint Recognition Based on Local DCT Feature Extraction
verfasst von : H. Kipsang Choge, Tadahiro Oyama, Stephen Karungaru, Satoru Tsuge, Minoru Fukumi
Erschienen in: Neural Information Processing
Verlag: Springer Berlin Heidelberg
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In this paper we present a method which extracts features from palmprint images by applying the Discrete Cosine Transform (DCT) on small blocks of the segmented region of interest consisting of the middle palm area. The region is extracted after careful preprocessing to normalize for position and illumination. This method takes advantage of the well known capability of the DCT to represent natural images using only a few coefficients by performing the DCT on each block. After ranking the coefficients by magnitude and selecting only the most prominent, these are then concatenated into a compact feature vector that represents each palmprint. Recognition and verification experiments using the PolyU Palmprint Database show that this is an effective and efficient approach, with a recognition rate above 99 % and Equal Error Rate (EER) of less than 3 %.