School of Computer Science and Technology, Shandong University, Jinan 250101, China.
Sensors (Basel). 2013 Aug 22;13(9):11243-59. doi: 10.3390/s130911243.
Finger veins are a promising biometric pattern for personalized identification in terms of their advantages over existing biometrics. Based on the spatial pyramid representation and the combination of more effective information such as gray, texture and shape, this paper proposes a simple but powerful feature, called Pyramid Histograms of Gray, Texture and Orientation Gradients (PHGTOG). For a finger vein image, PHGTOG can reflect the global spatial layout and local details of gray, texture and shape. To further improve the recognition performance and reduce the computational complexity, we select a personalized subset of features from PHGTOG for each subject by using the sparse weight vector, which is trained by using LASSO and called PFS-PHGTOG. We conduct extensive experiments to demonstrate the promise of the PHGTOG and PFS-PHGTOG, experimental results on our databases show that PHGTOG outperforms the other existing features. Moreover, PFS-PHGTOG can further boost the performance in comparison with PHGTOG.
指纹静脉在个性化识别方面具有许多优势，是一种很有前途的生物识别模式。本文基于空间金字塔表示，并结合灰度、纹理和形状等更有效的信息，提出了一种简单而强大的特征，称为灰度、纹理和方向梯度的金字塔直方图（PHGTOG）。对于手指静脉图像，PHGTOG 可以反映灰度、纹理和形状的全局空间布局和局部细节。为了进一步提高识别性能和降低计算复杂度，我们通过使用稀疏权值向量，为每个主体从 PHGTOG 中选择个性化的特征子集，该权值向量是通过 LASSO 训练得到的，称为 PFS-PHGTOG。我们进行了广泛的实验来验证 PHGTOG 和 PFS-PHGTOG 的潜力，在我们的数据库上的实验结果表明，PHGTOG 优于其他现有特征。此外，与 PHGTOG 相比，PFS-PHGTOG 可以进一步提高性能。
