Three-orientation joint features for multispectral palmprint recognition

Mengwen Li; Huabin Wang; Liang Tao

doi:10.1117/12.2589399

27 January 2021 Three-orientation joint features for multispectral palmprint recognition

Mengwen Li, Huabin Wang, Liang Tao

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117201J (2021) https://doi.org/10.1117/12.2589399
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Orientation feature is one of the most important features of palmprint images. At present, palmprint recognition methods based on orientation features have achieved promising recognition performance. However, most of these methods neglect the relationships between the orientation features, which can not effectively describe the structure of palm lines, and are sensitive to the translation and rotation. In this paper, a palmprint recognition method based on threeorientation joint features is proposed. Firstly, Gabor filter is adopted to extract the orientation features. Secondly, by analyzing the characteristics of palm lines, two sets of feature vectors are constructed by using three orientation features, which are maximum and two minimum orientation. Finally, the weighted Manhattan distance metric is used to measure the similarity between two palms. Further, in order to improve the recognition performance, a feature fusion scheme is proposed for fusing different features obtained from multispectral palmprints. Experiments on PolyU MSpalmprint Database demonstrate that the proposed method can achieve better recognition accuracy than some state-of-the-art methods.

Citation Download Citation

Mengwen Li, Huabin Wang, and Liang Tao "Three-orientation joint features for multispectral palmprint recognition", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117201J (27 January 2021); https://doi.org/10.1117/12.2589399

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