Synergy of foreground-background images for feature extraction: Offline signature verification using Fisher vector with fused KAZE features
Synergy of foreground-background images for feature extraction: Offline signature verification using Fisher vector with fused KAZE features
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DOI：
10.1016/j.patcog.2018.02.027
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Manabu Okawa
中科院分区：
文献类型：
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作者：
Manabu Okawa
离线签名验证已被接受为个人身份验证的工具。为了解决剩余的挑战，提高鉴别力，本研究提出了一种新的特征提取方法的基础上的Fisher向量（FV）与融合KAZE功能检测到的前景和背景签名图像，使用最近的融合策略。实验结果表明：（1）前景和背景签名图像的KAZE特征分别表现出良好的性能;（2）融合前景和背景签名图像的KAZE特征提高了性能;（3）采用FV提供了更精确的每个作者特征的空间分布;（4）当具有表示级融合的FV产生高维向量时，用于原始FV的主成分分析可以提供维度更紧凑的向量而没有显著的性能损失;（5）在MCYT-75签名数据集上，该方法的错误率明显低于现有离线签名验证方法。
Offline signature verification has been accepted as a tool for individual authentication. To address the remaining challenges and improve the discriminative power, this study proposes a new feature extraction approach based on a Fisher vector (FV) with fused KAZE features detected from both foreground and background signature images using a recent fusion strategy. Experimental results demonstrate the following: (1) KAZE features from foreground and background signature images show good performance, respectively; (2) fused KAZE features from foreground and background signature images improve performance; (3) adoption of the FV provides a more precise spatial distribution of the characteristics per writer; (4) while an FV with representation-level fusion produces a high-dimensional vector, principal component analysis for the original FV can provide a more dimensionally compact vector without significant performance loss; (5) with the popular MCYT-75 signature dataset, the proposed method yields significantly lower error rates than existing state-of-the-art offline signature verification methods.