一种基于小波变换提取拐点的手写签名认证方法

A Handwritten Signature Verification Method Based on Wavelet Transform to Pick up Inflection Points

为了对手写签名进行准确认证 ,介绍了一种基于小波变换的用于提取手写签名笔划坐标 -时间曲线拐点的手写签名认证方法 ,该方法首先采用以高斯函数的二阶导数作为小波基的小波变换技术来进行拐点提取 ,由于变换后的曲线可以近似认为是原函数的二次导数曲线 ,它不仅能够从签名笔划曲线中准确可靠地提取出具有特殊意义的拐点 ,并具有分辨尺度可调的优点 ,从而提高了认证的普适性 ;然后以该方法为基础 ,进行不同签名之间拐点序列的匹配 ;最后再利用提取的拐点来对签名进行分段和段 -段对应处理 .若由于利用拐点分段能体现出各分段局部相似性与稳定性不同的特点 ,因而拉开了真伪签名之间的差距 ,若进一步与动态时间弯曲算法相结合 ,即可取得比较满意的认证效果 .

This paper introduces a method to pick up inflection points in handwritten signature curves. If wavelet is defined as the second derivative of the Gaussian function, and the original curve is seemed as a continuous function, then the curve got after wavelet transform can be nearly seemed as the second derivative of the original function. It is widely known that where the second derivative of a function equals to zero corresponding to its inflection points, so the inflection points in the original curve can be picked up effectively by detecting zero points in its second derivative curve. Besides, because the wavelet has the advantage that it can provide multiple scales to analysis the signature curves, using this method, handwritten signature can be segmented and matched effectively. The distance between two corresponding segments can be measured with Dynamic Time Warping Algorithm (DTWA), which is a widely used dynamic matching algorithm in speech process and has good performance. Because the segmentation implemented by detecting inflection points has different comparability and stability, combined with DTWA, the method can improve the result of signature verification.