Abstract
This paper presents a new approach for inner-knuckle-print verification. Firstly, guided image filtering is implemented to remove noise and the minute lines. Then robust line features are extracted from the image based on a derivative edge detector. Finally the binary line images are matched by using a cross-correlation-based method. The experiments on a finger image database which includes 2000 images from 100 different individuals show good performance of the proposed approach.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 60903089, No. 60773062, No. 61100143, No. 60801053), Scientific Research Plan Projects of Hebei Educational Bureau (No. 2008312), Beijing Natural Science Foundation (No. 4082025), Science and Technology Support Program of Hebei Province (No. 12210137).
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Liu, M., Yan, J. (2013). Inner-Knuckle-Print Verification Based on Guided Image Filtering. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38466-0_53
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DOI: https://doi.org/10.1007/978-3-642-38466-0_53
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