Publication: Compactnet: a lightweight convolutional neural network for one-shot online signature verification
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Issued Date
2024-12-01
Resource Type
ISSN
14332833
eISSN
14332825
Scopus ID
2-s2.0-85194481855
Journal Title
International Journal on Document Analysis and Recognition
Volume
27
Issue
4
Start Page
671
End Page
682
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal on Document Analysis and Recognition Vol.27 No.4 (2024) , 671-682
Suggested Citation
Sae-Bae N., Chatwattanasiri N., Udomhunsakul S. Compactnet: a lightweight convolutional neural network for one-shot online signature verification. International Journal on Document Analysis and Recognition Vol.27 No.4 (2024) , 671-682. 682. doi:10.1007/s10032-024-00478-7 Retrieved from: https://hdl.handle.net/20.500.14740/20141
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Corresponding Author(s)
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Abstract
This paper proposes a method for the online signature verification task that allows the signature to be verified effectively using a single enrolled signature sample. The method utilizes a neural network with two one-dimensional convolutional neural network (1D-CNN) components to extract the vector representation of an online signature. The first component is a global 1D-CNN with full-length kernels. The second component is the standard 1D-CNN with partial length kernels that have been successfully used in many time-series classification tasks. The network is trained from a set of online signature samples to extract the vector representation of unknown signatures. The experimental results demonstrated that when using a vector representation derived from the proposed network, a single unseen enrolled signature sample achieved an Equal Error Rate (EER) of 4.35% when tested against authentic signatures of other users. This result indicates the effectiveness of the network in accurately distinguishing between genuine signatures and those of different users.