Abstract
Direction features server as one of the most important features of palmprint and there have been a number of direction-based palmprint recognition methods. However, most existing direction-based methods extract the dominant direction features, which are possibly not the most discriminative features due to the influence of the neighboring directions. In this paper, we present a straightforward example to show that the direction with a large neighboring direction response difference (NDRD) is more stable so as to be more robust and discriminative. Inspired by that, we propose a new feature descriptor by extracting multiple direction features with the competitive NDRDs for palmprint recognition. Extensive experiments conducted on three widely used palmprint databases, including the PolyU, IITD and CASIA databases, demonstrate the effectiveness of the proposed method.
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Acknowledgment
This work was supported inpart by the National Natural Science Foundation of China under Grants 61702110 and 61972102 and inpart by the Guangzhou Science and Technology Program under Grants 201802010042 and 201804010278.
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Qiu, Z. et al. (2019). Local Discriminative Direction Extraction for Palmprint Recognition. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_1
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DOI: https://doi.org/10.1007/978-3-030-31456-9_1
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