Ms. Mengwen Li Profile

Mengwen Li

at Huaibei Normal Univ

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Proceedings Article | 27 June 2023 Paper

Joint multi half-orientation features learning for contactless palmprint recognition

Mengwen Li, Huaiyu Liu, Qianqian Meng, Qing Wang

Proceedings Volume 12705, 127050B (2023) https://doi.org/10.1117/12.2680467

KEYWORDS: Pattern recognition, Biometrics, Feature extraction

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Contactless palmprint recognition attracted much attention in recent years for it is more user-friendly and sanitary compared with contact palmprint recognition. However, due to the lack of restrictions on the position of the palms when collecting images, there are severe translation and rotation in contactless palmprint images, which will seriously affect the recognition accuracy. Conventional palmprint recognition methods based on the hand-craft features mainly focus on the characteristics of palmprint images, but the correlations among samples are usually neglected. Therefore, it is urgent that extracting the stable and discriminative features to improve the recognition performance. To solve this problem, a joint multi half-orientation features learning method (JMHOFL) was proposed in this article. First, we extracted the orientation features using banks of half-Gabor filters, and constructed the multi half-orientation features (MHOF) of the palmprint image. To overcome the effects of translation and rotation, MHOF obtained multi orientation codes and performed block-wise statistics on these orientation codes. Afterwards, a joint low-rank inter-class sparsity least squares regression (JLRICS_LSR) was proposed to study more stable and discriminative features from MHOF. JLRICS_LSR takes into account the structure between samples, and reduces the influence of noises. Lastly, Euclidean distance is used for feature matching. Experiments on CASIA, IITD, and Tongji palmprint databases showed the promising performance of the proposed method.

Proceedings Article | 27 January 2021 Paper

Three-orientation joint features for multispectral palmprint recognition

Mengwen Li, Huabin Wang, Liang Tao

Proceedings Volume 11720, 117201J (2021) https://doi.org/10.1117/12.2589399

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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.

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