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.
Authors
- Nirmal Poudel (ORCID: https://orcid.org/0000-0002-1611-1149)
- Saugat Neupane
- Smarika Shrestha
- Subarna Shakya (ORCID: https://orcid.org/0000-0003-2268-0097)
- Suyasha Nepal
Institutions
- Nepal Development Research Institute (NP)
- Kantipur Engineering College
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