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- W4324137411 abstract "Among other types of biometric recognition systems like fingerprint, voice, iris, etc., signature verification is the most frequently used biometric type. Authenticating a handwritten signature can be made easier by using the proposed Signature Verification System (SVS), which minimizes human error. The proposed system helps in determining how closely the user's new signature resembles the previous signature stored in the database. Biometric verification technologies include voice recognition, iris scanning, face recognition, and finger print scanning, among others. However, not all systems are portable, and some have very high deployment costs. This study investigates the signature verification system that can be used to achieve beneficial results in order to increase efficacy, efficiency, and portability. This is accomplished by comparing the Structural Similarity Index Measure (SSIM) and the Convolutional Neural Network (CNN)." @default.
- W4324137411 created "2023-03-15" @default.
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- W4324137411 date "2023-01-23" @default.
- W4324137411 modified "2023-10-16" @default.
- W4324137411 title "Comparative Model of Offline Signature Verification System" @default.
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- W4324137411 doi "https://doi.org/10.1109/icssit55814.2023.10060946" @default.
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