Abstract
The robust-feature extraction has been an important problem in biometrics research. This issue is especially important for finger-based recognition method since the finger is prone to vary in pose during imaging. To reliably represent the multimodal finger-based biometric features, this paper proposes a novel feature extraction method based on the Gabor ordinal measure (GOM). Firstly, to obtain the illumination invariance feature of three modalities, finger print (FP), finger-vein (FV) and finger-knuckle-print (FKP), of a finger, the feature maps are respectively obtained using GOM. Secondly, the finger feature maps are granulated hierarchically in a bottom-up manner by varying granularity. The intension of each granulation is represented by Gabor-Ordinal-based Local-invariant Gray Features (GOLGFs). Finally, the experimental results show that the proposed method can achieve higher accuracy recognition in a large homemade database.
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Zhong, Z., Jia, G., Shi, Y., Yang, J. (2015). A Finger-based Recognition Method with Insensitivity to Pose Invariance. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_66
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DOI: https://doi.org/10.1007/978-3-319-25417-3_66
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