Abstract: As one of the important technologies in the field of biometric authentication, handwritten signature authentication technology has a wide application prospect. In order to improve the accuracy of handwritten signature verification, a method combining wavelet transform and CPN neural network is proposed. First, we take some preprocessing measures such as filtering and denoising, binarization, thinning, and normalization to the signature sample image, then the text image is decomposed by DB3 wavelet and the decomposed high pass coefficient matrix is extracted and treated as the features, then the CPN neural network classifier is used to train 7500 times for each training sample. Finally, the trained classifier is used to classify and identify the samples. On an experimental data set consisting of 36 identification experiment groups, the sample recognition accuracy of the method reached 93.48%. Comparative tests of various methods were used, the results show that the signature feature extraction of this paper is comprehensive and the recognition effect is better than the linear classifiers.

Key words:  , wavelet transform； feature extraction； neural network； signature verification； weight vector