Direction-based methods are the most powerful and popular palmprint recognition methods. However, there is no existing work that completely analyzes the essential differences among different direction-based methods and explores the most discriminant direction representation of a palmprint. In this paper, we attempt to establish the connection between the direction feature extraction model and the discriminability of direction features, and we propose a novel exponential and Gaussian fusion model (EGM) to characterize the discriminative power of different directions. The EGM can provide us with a new insight into the optimal direction feature selection of palmprints. Moreover, we propose a local discriminant direction binary pattern (LDDBP) to completely represent the direction features of a palmprint. Guided by the EGM, the most discriminant directions can be exploited to form the LDDBP-based descriptor for palmprint representation and recognition. Extensive experiment results conducted on four widely used palmprint databases demonstrate the superiority of the proposed LDDBP method over the state-of-the-art direction-based methods.

中文翻译：

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掌纹表示和识别的局部判别方向二元模式

基于方向的方法是最强大和流行的掌纹识别方法。然而，现有的工作还没有完全分析不同基于方向的方法之间的本质差异并探索掌纹的最具判别力的方向表示。在本文中，我们尝试建立方向特征提取模型与方向特征的可判别性之间的联系，并提出了一种新的指数和高斯融合模型（EGM）来表征不同方向的判别力。EGM 可以为我们提供对掌纹最优方向特征选择的新见解。此外，我们提出了一种局部判别方向二值模式（LDDBP）来完全表示掌纹的方向特征。在临时股东大会的指导下，可以利用最具辨别力的方向来形成基于 LDDBP 的描述子，用于掌纹表示和识别。在四个广泛使用的掌纹数据库上进行的大量实验结果证明了所提出的 LDDBP 方法优于最先进的基于方向的方法。