SmartKYC

This Paper demonstrates an automated system to streamline the KYC (Know Your Customer) process using machine learning. It integrates document validation, image pre-processing, text extraction, automated form filling, face detection, liveliness detection, and verification. Tesseract, utilizing LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network), extracts text from smart driving licenses to auto-fill digital KYC forms, reducing manual entry errors. MTCNN (Multi-task Cascaded Convolutional Networks) handles face detection, OpenCV ensures liveliness, and FaceNet (Inception-ResNet-v1) verifies the selfie against the ID photo for security. Successful verification grants confirmation; otherwise, users resolve issues. The system achieved 86.43% training and 86.25% validation accuracy, improving efficiency, accuracy, and user experience.

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Publication Details

Journal
International Journal on Engineering Technology
Published
2025-05-19
DOI
https://doi.org/10.3126/injet.v2i2.78620
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

SmartKYC

Nirmal Poudel, Saugat Neupane, Smarika Shrestha, Subarna Shakya et al.
International Journal on Engineering Technology
Face recognition and analysis
article

SmartKYC

Nirmal Poudel, Saugat Neupane, Smarika Shrestha, Subarna Shakya, Suyasha Nepal
article en

Abstract

This Paper demonstrates an automated system to streamline the KYC (Know Your Customer) process using machine learning. It integrates document validation, image pre-processing, text extraction, automated form filling, face detection, liveliness detection, and verification. Tesseract, utilizing LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network), extracts text from smart driving licenses to auto-fill digital KYC forms, reducing manual entry errors. MTCNN (Multi-task Cascaded Convolutional Networks) handles face detection, OpenCV ensures liveliness, and FaceNet (Inception-ResNet-v1) verifies the selfie against the ID photo for security. Successful verification grants confirmation; otherwise, users resolve issues. The system achieved 86.43% training and 86.25% validation accuracy, improving efficiency, accuracy, and user experience.

International Journal on Engineering TechnologyVol. 2(2)
Nepal Development Research Institute (NP), Kantipur Engineering College
Openalex Percentile: Top 91%
Face recognition and analysis
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