Off-line signature verification based on combination of direction feature and grid feature
方向特征和网格特征融合的离线签名鉴别

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.