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Dynamic Signature Verification Using Pattern Recognition

Journal of Software Engineering and Applications 2024年第5期17卷 214-227页

作者： Emmanuel Nwabueze Ekwonwune Duroha Austin Ekekwe Chinyere Iheakachi Ubochi henry chinedu oleribe Department of Computer Science Imo State University Owerri Nigeria Department of Computer Science Gregory University Abia Nigeria ICT Department Alvan Ikoku University of Education Owerri Nigeria

Dynamic signature is a biometric modality that recognizes an individual’s anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the TensorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

关键词： Verification Security Biometrics Signature Authentication Model Pattern Recognition Dynamic

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A rare combination of late-onset post-LASIK keratectasia after early-onset central toxic keratopathy

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International Journal of Ophthalmology(English edition) 2022年第11期15卷 1873-1877页

作者： Mehrdad Mohammadpour Masoud Khorrami-Nejad Mohsen Heirani Joseph oleribe Translational Ophthalmology Research Center Farabi Eye HospitalTehran University of Medical SciencesTehran ***Iran Department of Optometry Imo State UniversityOwerriImo 2000Nigeria

Dear Editor,We have presented the clinical findings of a case with bilateral early-onset central toxic keratopathy(CTK) followed by bilateral late-onset post-LASIK keratectasia. Laser in situ keratomileusis(LASIK) is a safe procedure to treat a broad spectrum of refractive errors;however, unexpected and rarely encountered post-LASIK complications could be a challenge for patient and surgeon.

关键词： rare refractive finding

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