关键词: 离线手写签名, 特征提取, 特征融合, 区间符号表示, 类内变异性, 模糊相似性

Abstract: In order to optimize off-line handwritten signature verification, an efficient off-line signature verification method based on interval notation and fuzzy similarity measure is presented. In the feature extraction step, a set of features based on an improved Local Binary Pattern（LBP） and a Gray Level Co-occurrence Matrix（GLCM） are computed from the signature image and its undersampled bitmap. Then obtaining the interval value symbol data for each feature in each signature class. A signature model consisting of a set of interval values（corresponding to the number of features） is created for each person’s handwritten signature class. In order to verify the test sample, a new fuzzy similarity measure is also proposed to calculate the similarity between the test sample signature and the corresponding interval-valued sign model. In order to evaluate the proposed verification method, different types of Chinese handwritten signature handwriting pictures are used for testing and comparison. The recognition rate can reach 92.75%. The experimental results show that when the number of training samples is 10 or more, the recognition rate is effectively improved. The advantage of the proposed method is that the proposed method does not need to be retrained when adding new classes to the system. The proposed method is cheaper than neural networks in terms of memory usage and computation time.

Key words: off-line handwritten signature, feature extraction, feature fusion, interval notation, intra-class variability, fuzzy similarity