摘要：

现有的基于方向模式的掌纹识别方法利用预定义的滤波器来获取掌纹图像中的线响应, 然而, 这种方法对丰富的先验知识依赖较强, 且常常忽略重要的方向信息, 还会造成维度过大的问题。为了解决以上问题, 提出一种基于Gabor滤波器和改进线性判别分析的掌纹识别方法。首先使用二维Gabor滤波器提取掌纹图像中的鲁棒卷积差分特征, 提取到的特征可以更充分地描述掌纹图像中每个像素的局部方向的变化。然后提出一种判别特征学习模型, 该模型通过最大化类间距离和最小化类内距离, 从局部方向特征中学习出判别特征, 在降低数据维度的同时减少噪声的影响。在PolyU、M_Blue、GPDS和IITD 4个公共掌纹数据库上进行实验, 其中在GPDS和IITD 2个非接触式掌纹数据库上的识别率分别达到96.80%和99.29%。实验结果表明, 提出的算法能够更有效地提取掌纹图像的判别特征, 并显著提高掌纹识别的准确度。

关键词: 掌纹识别, 特征选择, 特征提取, 线性判别分析, 方向模式学习

Abstract:

Existing palmprint recognition methods based on direction patterns use predefined filters to obtain line responses in palmprint images. However, this method relies heavily on rich prior knowledge, often ignores important direction information, and results in excessively large dimensionality. To solve the above problems, this paper proposes a palmprint recognition method based on Gabor filter and improved linear discriminant analysis. First, a two-dimensional Gabor filter is used to extract robust convolution differential features in palmprint images. The extracted features more fully describe the changes in the local orientation of each pixel in the palmprint image. Then, a discriminative feature learning model is proposed that learns discriminative features from local directional features by maximizing the inter-class distance and minimizing the intra-class distance, thereby reducing the impact of noise while reducing the data dimensionality. This paper conducts experiments on four public palmprint databases: PolyU, M_Blue, GPDS and IITD. The recognition rates on the two non-contact palmprint databases, GPDS and IITD, reach 96.80% and 99.29%, respectively. Experimental results show that the algorithm proposed in this paper can more effectively extract the discriminative features of palmprint images and significantly improve the accuracy of palmprint recognition.

Key words: palmprint recognition, feature selection, feature extraction, linear discriminant analysis, directional mode learning