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A multimodal authentication for biometric verification system using palmprints and fingers

机译：使用棕榈纹和手指的生物识别系统的多模态认证

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摘要

Trusted identification approaches play a critical role in everyday life and daily activities of humans. Hand as a physiological characteristic that has high acceptability and stability that contains several biometrics components has attracted the attention of many scholars so that almost all parts of it are considered as a member of the biometric system. The proposed method in this research is verification by a color image of the palm of the hand and the index, middle, ring and little fingers which is implemented by a new method for extracting texture features on images of 177 individuals from the Hong Kong Polytechnic University Contact-Free 3D/2D Hand Images Database. The proposed feature extraction method is the use of Turn Counts in each of Gabor's filters applied in different directions and scales on each of the RGB components of the images individually. Before classification, a binary genetic algorithm is applied to use the best combination of features for each color component. The system performance has been studied in unimodal and multimodal modes using the SVM classification and the best result is obtained based on combining the features of the palm and four fingers with an EER of 0.67 ± 0.13. Also, the effectiveness of this method has been investigated by generalizing the feature extraction method on other databases in this domain.
机译：值得信赖的识别方法在人类的日常生活和日常活动中发挥着关键作用。手作为具有含有若干生物识别成分的高可接受性和稳定性的生理特性，这些组件引起了许多学者的注意，因此几乎所有部分都被认为是生物识别系统的成员。本研究中的提出方法是通过手掌的彩色图像和指数，中间，环和小手指进行验证，该指数是由香港理工大学的177个个人的图像中提取纹理特征的新方法实现的联系方式3D / 2D手图像数据库。所提出的特征提取方法是在不同方向上施加的每个Gabor过滤器中的转弯计数，并单独地在图像的每个RGB组件上进行缩放。在分类之前，应用二进制遗传算法来使用每个颜色分量的最佳特征组合。已经在使用SVM分类的单峰和多模式模式中研究了系统性能，并且基于将手掌和四个手指的特征组合在0.67±0.13的eer的组合来获得最佳结果。此外，通过在该域中的其他数据库上推广特征提取方法来研究该方法的有效性。

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