CAJ | 학술논문

提出了一种基于小波包隐马尔可夫的脱机手写体签名识别方法.该方法用小波包对归一化的签名图像进行特征提取,用混合高斯模型刻画各频带的小波包的系数分布,并用隐马尔可夫的状态转移模型描述了高斯模型在各频带间的相关性和依赖性.该方法数据预处理简单,特征提取完全可逆,避免了复杂分割,很好地描述了签名图像的小波包分解的统计特性,实验表明具有较好的抗噪性、鲁棒性、适应性和较高的识别率.
제출료일충기우소파포은마이가부적탈궤수사체첨명식별방법.해방법용소파포대귀일화적첨명도상진행특정제취,용혼합고사모형각화각빈대적소파포적계수분포,병용은마이가부적상태전이모형묘술료고사모형재각빈대간적상관성화의뢰성.해방법수거예처리간단,특정제취완전가역,피면료복잡분할,흔호지묘술료첨명도상적소파포분해적통계특성,실험표명구유교호적항조성、로봉성、괄응성화교고적식별솔.
The paper proposed a way of off-line handwritten signature recognition based on wavelet packet and Hidden Markov Model (HMM). Wavelet packet was used to extract the features for the whole normalized signature image; the distribution of the wavelet packet coefficients could be approximated by mixture Gaussian model, and the state transfer model of HMM was adopted to describe the relevancy and dependency of each channel in the mixture Gaussian model. The data preprocessing is simple, and the feature extraction is complete and reversible. This method avoided complex segmentation and illuminated the decomposed statistical characteristics of the signature image. The experimental results show that the algorithm has better anti-noise ability, robustness and the recognition rate is higher.