基于非负稀疏表示的遮挡人耳识别

Ear Recognition under Random Occlusion via Non-negative Sparse Representation

遮挡是人耳识别中一个难以回避的问题,当人耳被遮挡时绝大多数人耳识别算法性能会大大降低.借鉴人类视觉认知特性,将非负稀疏表示用于遮挡情况下的人耳识别,提出一种更为鲁棒的遮挡人耳识别方法.首先对训练人耳图像和待识别人耳图像进行下采样降维,然后将待识别人耳图像表示为由所有训练人耳图像构成的字典的非负稀疏线性组合,最后通过求解非负稀疏表示模型得到稀疏表示系数,根据测试人耳图像的重建误差进行识别.在USTB人耳图像库上的实验结果表明,当人耳图像被遮挡时,该方法具有更好的鲁棒性和更高的识别率.

One challenging problem inevitable in real application is that the ears are often occluded by some objects such as hair or hat.In this paper,ageneral classification algorithm based on non-negative sparse representation is proposed to handle ear recognition under random occlusion.Unlike sparse representation based classification in which the input data are described as a combination of basis features involving both additive and subtractive components,the proposed classification paradigm expresses an input ear signal as a linear additive combination of all the training ear signals,and then classification is made according to the reconstruction error of the input ear image.The recognition performance for various levels of occlusion areas is investigated in which the location of occlusion is randomly chosen to simulate real scenario.Experimental results on USTB ear database reveal that when the ear is occluded,the proposed method exhibits great robustness and achieves better recognition performance.