基于新轮廓特征的离线签名鉴别

Off-line signature verification system based on novel contour features

离线签名笔画内部点及背景点的局部二值模式(LBP)非常相近,且对反映离线签名笔画特征有较大干扰,因此提出了一种轮廓处LBP直方图特征。提取签名轮廓上的LBP特征,同时引入了新的规则去除部分无用模式,可有效地提升LBP的有效性和鲁棒性。另外,针对方向链码特征在应用于签名鉴别时存在局限性的问题,提出了一种轮廓模式共生直方图特征。融合这两种轮廓特征,并使用主成分分析(principal component analysis,PCA)降维。最后,使用支持向量机分别在MCYT和GPDS两种公开离线签名数据库上进行测试,取得的平均错误率分别为13.51%和12.97%。在相同的数据集上与其他方法相比,具有更低的平均错误率。

In the off-line signature,LBP of background pixels,as well as pixels inside the strokes,accounting for a large proportion in a signature bitmap,are nearly the same.Consequently,they have great interference to describe the characteristics of signature’s strokes.So this paper proposed LBPC-based feature that mainly calculated the histogram of the local binary patterns on signature’s contour and removed some useless patterns by adding a rule to improve the effectiveness and robustness of LBP.Besides,seen as an improvement for directional chain code for its limitations in the signature verification,this paper introduced LCPC-based feature which aimed at computing the statistical features of the local contour patterns co-occurrence.Then,it applied the PCA to two above combined features due to the huge dimensionality.Finally,to evaluate the performance of proposed method,using SVM classifier,it conducted experiments on MCYT and GPDS open databases and the achievable average error rates were 13.51%and 12.97%respectively.Moreover,comparisons with other methods on the same datasets provide evidence that the proposed method obtains lower average error rate than others.