Lin Chih-Lung, Wang Shih-Hung, Cheng Hsu-Yung, Fan Kuo-Chin, Hsu Wei-Lieh, Lai Chin-Rong
Department of Electronic Engineering, Hwa Hsia University of Technology, 111 Gon Jhuan Rd., Chung Ho dist., New Taipei City 23568, Taiwan.
Institute of Computer Science and Information Engineering, National Central University, Taoyuan City 32001, Taiwan.
Sensors (Basel). 2015 Dec 12;15(12):31339-61. doi: 10.3390/s151229856.
In this paper, we present a reliable and robust biometric verification method based on bimodal physiological characteristics of palms, including the palmprint and palm-dorsum vein patterns. The proposed method consists of five steps: (1) automatically aligning and cropping the same region of interest from different palm or palm-dorsum images; (2) applying the digital wavelet transform and inverse wavelet transform to fuse palmprint and vein pattern images; (3) extracting the line-like features (LLFs) from the fused image; (4) obtaining multiresolution representations of the LLFs by using a multiresolution filter; and (5) using a support vector machine to verify the multiresolution representations of the LLFs. The proposed method possesses four advantages: first, both modal images are captured in peg-free scenarios to improve the user-friendliness of the verification device. Second, palmprint and vein pattern images are captured using a low-resolution digital scanner and infrared (IR) camera. The use of low-resolution images results in a smaller database. In addition, the vein pattern images are captured through the invisible IR spectrum, which improves antispoofing. Third, since the physiological characteristics of palmprint and vein pattern images are different, a hybrid fusing rule can be introduced to fuse the decomposition coefficients of different bands. The proposed method fuses decomposition coefficients at different decomposed levels, with different image sizes, captured from different sensor devices. Finally, the proposed method operates automatically and hence no parameters need to be set manually. Three thousand palmprint images and 3000 vein pattern images were collected from 100 volunteers to verify the validity of the proposed method. The results show a false rejection rate of 1.20% and a false acceptance rate of 1.56%. It demonstrates the validity and excellent performance of our proposed method comparing to other methods.
在本文中,我们提出了一种基于手掌双峰生理特征(包括掌纹和掌背静脉模式)的可靠且稳健的生物特征验证方法。所提出的方法包括五个步骤:(1)从不同的手掌或掌背图像中自动对齐并裁剪相同的感兴趣区域;(2)应用数字小波变换和逆小波变换来融合掌纹和静脉模式图像;(3)从融合图像中提取线状特征(LLF);(4)使用多分辨率滤波器获得LLF的多分辨率表示;(5)使用支持向量机验证LLF的多分辨率表示。所提出的方法具有四个优点:第一,两种模态图像均在无钉场景下采集,以提高验证设备的用户友好性。第二,掌纹和静脉模式图像使用低分辨率数字扫描仪和红外(IR)相机采集。使用低分辨率图像会导致数据库较小。此外,静脉模式图像通过不可见的红外光谱采集,这提高了防伪能力。第三,由于掌纹和静脉模式图像的生理特征不同,可以引入混合融合规则来融合不同波段的分解系数。所提出的方法融合了从不同传感器设备捕获的、具有不同图像大小的不同分解级别的分解系数。最后,所提出的方法自动运行,因此无需手动设置参数。从100名志愿者那里收集了3000张掌纹图像和3000张静脉模式图像,以验证所提出方法的有效性。结果显示错误拒绝率为1.20%,错误接受率为1.56%。与其他方法相比,它证明了我们所提出方法的有效性和优异性能。