指关节纹比手掌特征更明显,针对这种生物特征提出一种基于Gabor-带限相位相关(Gabor-BLPOC)的指关节纹识别算法.首先,使用Gabor滤波器抑制噪声,并采用限制对比度自适应直方图均衡化对指关节纹图像进行增强;其次,使用BLPOC算法提取指关...指关节纹比手掌特征更明显,针对这种生物特征提出一种基于Gabor-带限相位相关(Gabor-BLPOC)的指关节纹识别算法.首先,使用Gabor滤波器抑制噪声,并采用限制对比度自适应直方图均衡化对指关节纹图像进行增强;其次,使用BLPOC算法提取指关节纹图像的相位特征;然后,通过计算2幅指关节纹图像的互功率谱对指关节纹图像进行校准;最后,再次计算校准后图像的BLPOC,根据2幅图像的互功率谱峰值进行指关节纹图像的匹配.通过在Poly U FKP数据库上的实验表明,所提出算法的等错误率为1.57%,具有更加精确的匹配效果,从而验证了该算法的有效性.展开更多
Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach...Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.展开更多
文摘指关节纹比手掌特征更明显,针对这种生物特征提出一种基于Gabor-带限相位相关(Gabor-BLPOC)的指关节纹识别算法.首先,使用Gabor滤波器抑制噪声,并采用限制对比度自适应直方图均衡化对指关节纹图像进行增强;其次,使用BLPOC算法提取指关节纹图像的相位特征;然后,通过计算2幅指关节纹图像的互功率谱对指关节纹图像进行校准;最后,再次计算校准后图像的BLPOC,根据2幅图像的互功率谱峰值进行指关节纹图像的匹配.通过在Poly U FKP数据库上的实验表明,所提出算法的等错误率为1.57%,具有更加精确的匹配效果,从而验证了该算法的有效性.
文摘Finger Knuckle Print biometric plays a vital role in establishing security for real-time environments. The success of human authentication depends on high speed and accuracy. This paper proposed an integrated approach of personal authentication using texture based Finger Knuckle Print (FKP) recognition in multiresolution domain. FKP images are rich in texture patterns. Recently, many texture patterns are proposed for biometric feature extraction. Hence, it is essential to review whether Local Binary Patterns or its variants perform well for FKP recognition. In this paper, Local Directional Pattern (LDP), Local Derivative Ternary Pattern (LDTP) and Local Texture Description Framework based Modified Local Directional Pattern (LTDF_MLDN) based feature extraction in multiresolution domain are experimented with Nearest Neighbor and Extreme Learning Machine (ELM) Classifier for FKP recognition. Experiments were conducted on PolYU database. The result shows that LDTP in Contourlet domain achieves a promising performance. It also proves that Soft classifier performs better than the hard classifier.
