Dynamic and static feature fusion for increased accuracy in signature verification
作者：
Highlights：
• The sound arising from the friction of pen and paper during the signing is assessed.
• A dataset containing sound files and signature image files was built with 75 signers.
• The proposed method applied for only sound, only image, and fusion of these two data.
• When verification is made with only sound data, the EERs are between 0.09% and 6%.
• Results show that the fusion of signature sound and signature image decreases EERs.
摘要
•The sound arising from the friction of pen and paper during the signing is assessed.•A dataset containing sound files and signature image files was built with 75 signers.•The proposed method applied for only sound, only image, and fusion of these two data.•When verification is made with only sound data, the EERs are between 0.09% and 6%.•Results show that the fusion of signature sound and signature image decreases EERs.
论文关键词：Score-level fusion,Signature verification,Image processing,Audio signal processing,Support vector machines,Convolutional neural networks
论文评审过程：Received 16 November 2021, Revised 6 May 2022, Accepted 8 July 2022, Available online 13 July 2022, Version of Record 23 July 2022.
论文官网地址：https://doi.org/10.1016/j.image.2022.116823
