融合潜在纹理和表面一致性的三维掌纹识别

针对三维 (3D)掌纹识别由于噪声干扰和忽略相邻深度信息引起识别率低的问题,提出融合潜在纹理和表面一致性的3D掌纹识别方法。首先,利用能量局部边缘二值码(energy local edge binary code,ELEBC)从能量图中提取潜在的纹理方向信息,消除噪声。然后,通过平均块模式表面类型(mean block pattern surface type,MBST)获取表面一致性。最后,利用主成分分析(principal component analysis,PCA)进行数据降维,并使用决策级融合,从而获取最终的识别结果。在香港理工大学3D掌纹数据库中进行相关实验,结果表明,正确识别率最高可达到99.71%,相比于其他新颖算法具有优势,并且识别分类时间保持在0.5 s以下。这显示出本文方法不仅具有良好的识别效果,同时能够满足实时性的要求,具有应用价值。

Potential texture and surface consistency extraction for 3D palmprint recognition

In view of the problem of low recognition accuracy caused by 3D palmprint recognition due to noise interference and neglect of adjacent depth information,a 3D palmprint recognition method combining potential texture and surface consistency is proposed.First,the energy local edge binary code (ELEBC) is used to extract the potential texture direction information from the energy map to eliminate the noise.Then,surface consistency is obtained by mean block pattern surface type (MBST).Finally,the principal component analysis (PCA) is used to reduce the data dimension,and the final recognition result is obtained after decision-level fusion.Relevant experiments are carried out using the 3D palmprint database of Hong Kong Polytechnic University,and the results show that it has significant advantages over other novel algorithms.The maximum correct recognition rate can reach 99.71%,and the recognition classification time is kept below 0.5 s.Therefore,the proposed scheme not only has accurate recognition effect,but also can meet the real-time requirements,and its application value is obvious.