KAZE features via fisher vector encoding for offline signature verification
KAZE features via fisher vector encoding for offline signature verification
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DOI：
10.1109/btas.2017.8272676
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发表时间：
2017-10
期刊：
影响因子：
--
通讯作者：
Manabu Okawa
中科院分区：
文献类型：
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作者：
Manabu Okawa
手写签名用于身份认证的广泛使用导致了对自动验证系统的需求。然而，与人类分析员，特别是法医文件审查员在各种条件下的表现相比，这些自动化系统的表现仍有很大的改进余地。此外，即使使用最新的技术，从有限数量的样本中获取尽可能多的信息仍然具有挑战性。为了应对这些挑战，提高离线签名验证的鉴别力，在最近的Fisher向量(FV)编码的基础上，提出了一种利用KAZE特征的离线签名验证方法。概率视觉词汇和高阶统计量的采用都可以编码关于KAZE特征分布的详细信息，为我们提供了更精确的特征空间分布。在公开的MCYT-75数据集上的实验结果总结如下：1)与最近提出的基于局部聚集描述符矢量(VLAD)的方法相比，该方法的性能有所提高。2)对原始FV使用主成分分析(PCA)可以提供维度更紧凑的向量，而不会显著降低性能。3)将该方法应用于MCYT-75数据集，与现有的离线签名验证系统相比，具有更低的错误率。
The widespread use of handwritten signatures for identity authentication has resulted in a need for automated verification systems. However, there is still significant room for improvement in the performance of these automated systems when compared with the performance of human analysts, particularly forensic document examiners, under a wide range of conditions. Furthermore, even with recent techniques, obtaining as much information as possible from a limited number of samples still remains challenging. In this study, to tackle these challenges and to boost the discriminative power of offline signature verification, a new method using KAZE features based on the recent Fisher vector (FV) encoding is proposed. The adoption of a probabilistic visual vocabulary and higher-order statistics, both of which can encode detailed information about the distribution of KAZE features, provides us with a more precise spatial distribution of the characteristics for a writer. The experimental results on the public MCYT-75 dataset can be summarized as follows: 1) The proposed method improves performance compared to the recent vector of locally aggregated descriptors (VLAD)-based approach. 2) The use of principal component analysis (PCA)for the original FV can provide a more dimensionally compact vector without a significant loss in performance. 3) The proposed method provides much lower error rates than existing state-of-the-art offline signature verification systems when applied to the MCYT-75 dataset.