用于手写签名识别的小波包混合高斯模型

为了解决手写体签名识别中数据预处理复杂、分割困难、特征提取不充分等问题，提出了基于小波包分解与高斯模型的脱机手写体签名识别方法。它对归一化的整个签名图像进行小波包分解，对分解值自动聚类和高斯建模，不但没有去噪、旋转、平移、分割的过程，而且特征提取完全且是可逆的分解过程。实验表明提出方法比其他方法具有更好的抗噪性、鲁棒性、适应性和识别率，为含噪脱机手写体签名识别提供了一种可行的技术解决方案。

Wavelet packs and Guass model for off-line handwritten signature recognition

The paper proposes a way of off-line handwritten signature recognition based on wavelet packs and Gauss model in order to solve the problems of complicated data preprocessing，difficult segmentation and deficient feature extraction.It decomposes the whole signature image with wavelet packs after normalized，clusters the decomposed values and gains Gauss modeling.It doesn’t need noise reduction，rotation，translation and segmentation，and feature extraction is complete and is reversible decomposed course.The results show that the method proposed has better antinoise，flexibility than other methods and improves the recognition rate.It provides a feasibility techno-project for off-line handwritten signature.