基于LDP和PNN的掌纹识别算法

Research of Palmprint Identification Algorithm Based on LDP and PNN

针对现有的掌纹识别算法对掌纹图像的旋转、尺度和亮度变化缺乏足够的鲁棒性,而且识别速度较慢的问题,本文通过LDP算子进行特征提取,将掌纹分成若干子区域后,然后通过连接这些子区域的LDP直方图生成掌纹特征向量,使得发生变化的同一类掌纹图像的相似性变大。为了能够提高识别精度且加快识别速度,通过概率神经网络(PNN)来进行分类。实验表明该算法对掌纹图像的旋转、尺度和亮度的变化有良好的鲁棒性,且提高识别率,识别速度较快。

To alleviate the limitations that the existing palmprint recognition methods are time-consuming, and their robustness to the variations of orientation, position and illumination is insufficient, this paper uses LDP operator to get feature extraction. The paimprint image is divided into sub-regions. Then connecting these sub-regions LDP histogram to generate palmprint feature vector, this can increase the similarity of the same type of palmprint image. In order to improve the recognition accuracy and accelerate the recognition speed, the classification is performed by Probabilistic Neural Networks (PNN). It is also shown that the proposed approach is robust to the variations of orientation, position and illumination and improves the recognition rate, and accelerates the recognition speed.