Implementation of Deepface Recognition Using Insight Face For E-Commerce Web Application

Concerns regarding data security and identity verification are emerging as a result of the fast
growth of online transactions and deliveries in e-commerce platforms. Conventional authentication
techniques, such passwords and one-time passwords, are susceptible to identity theft and SIM
swapping. This paper presents an Insight face-based facial authentication system for safe e-commerce
applications in order to address these issues. The suggested solution ensures a smooth and safe user
experience by substituting facial recognition with OTP-based verification at the stages of user login
and product delivery confirmation. With liveness detection and anti-spoofing features to stop
unwanted access, Insight Face is trained and refined to precisely recognize and validate user faces.
Furthermore, the system may be easily integrated with e-commerce platforms through API calls
because it is modularized as a custom PyPI module. By removing reliance on outside verification channels, this deep learning-based method improves authentication reliability, boosting user
confidence and operational effectiveness. The findings show that Insightf face biometric
authentication may greatly increase accuracy, lower rates of erroneous acceptance or rejection, and
offer real-time verification appropriate for extensive e-commerce networks.
Keywords: Deep Learning, Convolutional Neural Networks (CNNs), E-commerce Security,
Biometric, Liveness Detection, PyPI Library, Anti-spoofing, OTP Replacement