基于改进二维主成分分析及神经网络的人耳识别方法

针对人耳识别特征提取阶段二维主成分分析算法(2DPCA)所提取的人耳特征维数较大,从而造成实时性差、数据存储空间不足等问题提出了一种改进方法。该方法首先对人耳图片进行预处理,然后采用改进的两级2DPCA算法,进一步压缩提取的人耳特征维数,最后采用BP神经网络进行分类识别。实验表明,将改进的两级2DPCA算法同BP神经网络相结合,具有较好的实时性,同时节约了特征数据的存储空间,并保持了较好的识别率。

Ear recognition based on improved 2D principal component analysis and neural network

Since the human ear feature extraction method of 2 Dimensional Principal Component Analysis (2DPCA) gets relatively bigger features dimension, which leads to poor real-time capability and lack of data storage space, the authors proposed a new approach. First of all, pretreatment of human ear pictures was completed. Then an improved 2DPCA algorithm was used to compress feature dimension. Finally, BP neural network was used in ear classification. Experimental results show that this method is of real-time and can save feature data storage space, and also maintains the recognition rate.