基于二维和三维信息融合的人耳识别

2D and 3D information fusion based ear recognition

针对人耳识别中存在姿态、光照变化等问题,提出信息融合的方法,将二维人耳和三维人耳的信息进行融合,以克服姿态、光照对人耳识别的影响.对于二维人耳,由于姿态等的变化会导致人耳图像数据在高维空间中呈现出非线性流形结构,采用等距映射这种流形学习算法进行特征提取,对三维深度人耳则采用3D局部二值模式进行特征提取,然后分别进行二维和三维人耳识别,最后在决策层进行融合识别.在79人的人耳数据库上进行了实验,每人8幅带姿态的二维人耳图像和6幅带光照的三维人耳深度图像.实验结果表明,与单独的二维人耳和三维人耳识别相比,融合之后的识别效果和认证效果均有很大的改善.

In order to solve pose and illumination variation problems in ear recognition, an information fusion method was proposed to fuse 2D and 3D ear information at the decision level. For a 2D ear, the ear images will become nonlinear manifold structure due to pose variation, so the manifold learning method, isometric mapping （Isomap）, was used to extract features. For a 3D ear, the 3D local binary pattern （3DLBP） method was adopted for feature extraction. Then 2D ear recognition and 3D ear recognition were implemented separately. Finally, results from the 2D and 3D were fused at the decision level. Experiments were done on a database ~f 79 persons, one of which has eight 2D ears with pose variation and six 3D ears with illumination variation. It is found that both the recognition rate and verification rate are significantly improved compared with 2D ear recognition and 3D ear recognition alone.