Computer Vision-Based Signature Forgery Detection System Using Deep Learning: A Supervised Learning Approach

Reyes, Ryan Carreon; Polinar, Myriam J.; Dasalla, Richardson M.; Zapanta, Godofredo S.; Melegrito, Mark P.; Maaliw, Renato R.

doi:10.1109/conecct55679.2022.9865776

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…VGG-19 stands out as a convolutional neural network design distinguished by its profound structure, comprising 19 layers that encompass both convolutional and fully connected layers. It garnered recognition in the realm of computer vision assignments and image classification contests due to its uncomplicated yet impressive performance [13]. The distinctive trait of the VGG-19 architecture lies in its employment of compact 3x3 convolutional filters, arranged sequentially, which enhances its capability to grasp intricate attributes within images [14].…”

Section: Methodology Proposed Modelmentioning

confidence: 99%

Offline-Signature Verification System using Transfer Learning VGG-19

Tanvir,

Khan,

Ataur

et al. 2022

IJRISE

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Nowadays, Signature verification is one of the most common and effective biometric systems that used to recognize people in many institutions. In modern era of technology, advanced neural networks have provided us an option to solve this issue. In this study, The Robinreni Signature Dataset was utilized to classify the signatures of 64 people, each of whom had 64 original signatures and 64 fake signatures. One of the most popular CNN architecture, namely, VGG19, were used. Firstly, the dataset was distributed accordingly 1649 and 500 for training and validation. Secondly, preprocess the data to train the model. After that the model training process is started using transfer learning approach. Obtained experimental results that VGG19 is best suited for datasets with a validation accuracy of 98.79%.. Everyone has their own unique signature that used to identify and verify important documents and legal transactions. Our study shows the effectiveness of VGG19 for Signature Verification task. The findings will aid in the development of more effective Deep Learning-based signature verification methods.

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Section: Methodology Proposed Modelmentioning

confidence: 99%

Offline-Signature Verification System using Transfer Learning VGG-19

Tanvir,

Khan,

Ataur

et al. 2022

IJRISE

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“…In [17], pictures and smart computer methods were used to address CoViD-19 proximity issues. In [18], a method was introduced to improve signature verification, achieving an accuracy of 85-95%. In [19], special computer programs were investigated to find important parts of people's bodies when they are close to robots.…”

Section: Wwwetasrcom Sajini and Pushpa: Sensor Enabled Proximity Dete...mentioning

confidence: 99%

Sensor Enabled Proximity Detection with Hybridisation of IoT and Computer Vision Models to Assist the Visually Impaired

Sajini,

Pushpa

2023

Eng. Technol. Appl. Sci. Res.

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Proximity Detection Systems (PDS) are used to detect objects or persons close to Visually Impaired (VI) persons. Sensors are used to identify proximity based on the distance from objects. This study aimed to design a hybrid proximity detection framework for VI people using ultrasonic sensors embedded in a Raspberry Pi board to detect the proximity of a VI user in an office environment. Hybridization was based on the integration of IoT-enabled devices, ultrasonic proximity sensors, and computer vision algorithms to control the detection of objects or people and inform the user with a voice message. The model framework was implemented with 100 samples and tested with 10 analyses in each sample. The results showed significant improvement in detecting the proximity of the objects with an accuracy of 98.7%, outperforming current PDS with good results in precision, range, obstacle recognition, false positives and negatives, response time, usability, durability, reliability, etc.

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