Local apparent and latent direction extraction for palmprint recognition
Local apparent and latent direction extraction for palmprint recognition
复制标题
用于掌纹识别的局部表观和潜在方向提取
DOI：
10.1016/j.ins.2018.09.032
复制
发表时间：
2019-01-01
影响因子：
8.1
通讯作者：
Teng, Shaohua
中科院分区：
文献类型：
--
作者：
Fei, Lunke;Zhang, Bob;Teng, Shaohua
掌纹的方向信息为掌纹识别提供了最有前途的特征之一。然而，现有的基于方向的方法只从原始掌纹图像中提取表面方向特征，而忽略了掌纹图像卷积层的潜在方向特征。本文提出了一种新的用于掌纹识别的双层方向提取方法。该方法首先从掌纹的表层提取视向。然后，它进一步利用潜在的方向特征的能量地图层的视方向。最后，利用乘法和加法的方法，将视方向特征和潜方向特征合并为直方图特征描述子，用于掌纹识别。该方法在四个基准掌纹数据库（即理大、IITD、GPDS和CASIA掌纹数据库）上实现了最先进的性能。特别是，潜在的能量方向特征显示了一个有前途的性能，噪声掌纹图像识别。(C)2018爱思唯尔公司All rights reserved.
Direction information of the palmprint provides one of the most promising features for palmprint recognition. However, more existing direction-based methods only extract the surface direction features from raw palmprint images and ignore the informative latent direction feature of the convolution layer of palmprint images. In this paper, we propose a novel double-layer direction extraction method for palmprint recognition. The method first extracts the apparent direction from the surface layer of a palmprint. Then, it further exploits the latent direction features from the energy map layer of the apparent direction. Lastly, by using the multiplication and addition schemes, the apparent and latent direction features are pooled as the histogram feature descriptor for palmprint recognition. The proposed method achieves state-of-the-art performance on four benchmark palmprint databases, namely the PolyU, IITD, GPDS and CASIA palmprint databases. In particular, the latent energy direction feature shows a promising performance for noisy palmprint image recognition. (C) 2018 Elsevier Inc. All rights reserved.