Crease's discriminating power in palmprint recognition

Junbo Chen; Changshui Zhang; Gang Rong

doi:10.1117/12.441623

24 September 2001 Crease's discriminating power in palmprint recognition

Junbo Chen, Changshui Zhang, Gang Rong

Proceedings Volume 4554, Object Detection, Classification, and Tracking Technologies; (2001) https://doi.org/10.1117/12.441623
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

Abstract

The term crease is used to denote the total line features in high resolution palmprint images. In the bibliography, line features in low resolution palmprint images have been used in palmprint recognition. The result is limited due to the lack of lines in the poor images. In the high resolution situation, the creases are abundant while they also need much more deliberate treatment. A palmprint recognition system have been constructed in our previous research, which demonstrates the feasibility of palmprint recognition using only crease while it doesn't take into account many low quality images in reality. In this paper, we advance the work in image preprocessings, automatic threshold selection in the key part of system, and crease lines matching. Now the crease extraction and palmprint recognition results are exciting even in very low quality palmprint. Based on our system, an absolute threshold of similarity degree, which ensures all the image pairs with similarity degree above it are of the same palmprint, like that of matching point number in the fingerprint recognition, is figured out. All the results show that creases in high resolution palmprint images will be an outstanding candidate in the future palmprint recognition system with very high sample capacity.

Citation Download Citation

Junbo Chen, Changshui Zhang, and Gang Rong "Crease's discriminating power in palmprint recognition", Proc. SPIE 4554, Object Detection, Classification, and Tracking Technologies, (24 September 2001); https://doi.org/10.1117/12.441623

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KEYWORDS

Image resolution

Image segmentation

Fingerprint recognition

Digital filtering

Detection and tracking algorithms

Feature extraction

Image filtering

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