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Robust GrayScale Distribution Estimation for Contactless Palmprint Recognition

机译:无接触式掌纹识别的鲁棒灰度分布估计

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More and more research have been developed very recently for automatic hand recognition. This paper proposes a new method for contactless hand authentication in complex images. Our system uses skin color and hand shape information for an accurate hand detection process. Then, the palm is extracted and characterized by a robust and normalized decomposition. During enrollment, a distribution estimation is used to defined the optimal discrimination of the palmprint features. Finally, some specific thresholds are defined to separate in test phase impostor and genuine users. The experimental results present an error rate of 1.5% with a population of 49 people.
机译:最近已经开发了越来越多的研究来自动识别。本文提出了一种在复杂图像中的非接触式手部认证的新方法。我们的系统使用肤色和手形形状信息进行准确的手动检测过程。然后,通过稳健和归一化分解来提取手掌并表征。在注册期间,分发估计用于定义掌纹特征的最佳辨别。最后,定义了一些特定的阈值以在测试阶段冒名顶替者和真正的用户中分开。实验结果呈现出1.5%的错误率,人口49人。

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