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.