离线签名鉴别是一种重要的生物特征识别技术,提出了基于方向特征和网格特征融合的方法。网格特征广泛使用在图像的特征提取中,方向特征和网格特征结合不仅可以描述签名图像特殊点的方向和位置,还可以统计方向位置分布信息。两种特征组合会形成高维特征,然后使用主成分分析法进行降维,采用支持向量机作为分类器。该方法在签名数据库上进行评估,其结果表明,该方法能有效的提高离线签名鉴别的正确率。

Off-line signature verification is an important form of behavioral biometric identification. We present a method utilizing direction feature and grid feature to tackle the problem. Grid feature has been widely used as one of the mainstream feature extraction approach. The combination of direction feature and grid feature can not only describe the direction and location of the special point, but also record the distribution information of the location of the direction. In order to get features with lower dimensions, principal component analysis is employed to reduce redundant dimensions. In addition, we adopt support vector machines as classifiers for verification process. The proposed strategy is evaluated on the public signatute data bases. Experimental results have demonstrated that the proposed method is effective to improve off-line signature verification accuracy.