A new wrapper feature selection method for language-invariant offline signature verification
作者：
Highlights：
• Developed a language invariant signature verification framework.
• Converted signature images to signals utilizing singular value decomposition.
• Extracted features from signals rather than images unlike other methods.
• Used Red Deer Algorithm as a meta-heuristic wrapper feature selection method.
• Achieved satisfactory performance as compared to some state-of-the-art methods.
摘要
•Developed a language invariant signature verification framework.•Converted signature images to signals utilizing singular value decomposition.•Extracted features from signals rather than images unlike other methods.•Used Red Deer Algorithm as a meta-heuristic wrapper feature selection method.•Achieved satisfactory performance as compared to some state-of-the-art methods.
论文关键词：Offline signature verification,Wrapper feature selection,Red Deer Algorithm,Biometric,Meta-heuristic optimization
论文评审过程：Received 1 July 2020, Revised 12 March 2021, Accepted 9 August 2021, Available online 27 August 2021, Version of Record 2 September 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115756
