期刊
PATTERN RECOGNITION
卷 79, 期 -, 页码 480-489出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2018.02.027
关键词
Biometrics; Forensics; Signature verification; Fisher vector; KAZE features; Fusion strategy; Support vector machine
资金
- JSPS KAKENHI [JP 17H00368]
- Grants-in-Aid for Scientific Research [17H00368] Funding Source: KAKEN
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. (C) 2018 Elsevier Ltd. All rights reserved.
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