Online Signature Verification using Convolutional Neural Network (CNN) and OpenCV
This paper studies online signature verification on touch interface-based mobile devices. A simple and effective method for signature verification is developed. An online signature is represented with a discriminative feature vector derived from attributes of several histograms that can be computed in linear time. The resulting signature template is compact and requires constant space. The algorithm was first tested on the well-known MCYT-100 and SUSIG data sets. The results show that the performance of the proposed technique is comparable and often superior to state-of-the-art algorithms despite its simplicity and efficiency. In order to test the proposed method on signatures on camera capture devices, a data set was collected from an uncontrolled environment and over multiple sessions. Experimental results on this data set confirm the effectiveness of the proposed algorithm in mobile settings. The results demonstrate the problem of within-user variation of signatures across multiple sessions and the effectiveness of cross session training strategies to alleviate these problems.
Features Extraction process
- Static approaches-The static one involves geometric measures of the signature,
- Pseudo-dynamic approaches-Automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels.
- Accuracy is less.
- Illumination occurrences is less
- Error Rate is more
Behavioral Biometric Application
- Online signature (Data Set Images)
- Template aging,
Signature Verification Using CNN
- Python Idle
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