Face Recognition-Based Login Security System Using Deep Learning

Adi Sopian; Septiana Ningtyas; Usanto S.

doi:10.35877/454RI.jinav4886

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JINAV: Journal of Information and Visualization

Vol. 7 No. 2 (2026), Pages 205-216 · 2026-08-31

OriginalPaper | Open access | Published: August 31, 2026

Face Recognition-Based Login Security System Using Deep Learning

Adi Sopian, Septiana Ningtyas, Usanto S.

JINAV: Journal of Information and Visualization, Vol. 7 No. 2 (2026), pp. 205-216 https://doi.org/10.35877/454RI.jinav4886 Published: 2026-08-31

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Abstract

Password-based authentication remains the dominant access-control mechanism for digital systems, yet it suffers from well-documented weaknesses including weak or reused credentials, phishing, credential leakage, and the cognitive burden of password management. This study proposes and evaluates a face recognition-based login security system built on deep learning to address these limitations through a biometric factor that is intrinsic to the user. The system follows a verification pipeline consisting of webcam image acquisition, preprocessing, face detection, alignment, deep feature extraction, and similarity-based decision making. A convolutional neural network (CNN) of the FaceNet family maps each detected face to a compact 128-dimensional embedding, and authentication is performed by comparing the live embedding against an enrolled template using a Euclidean-distance threshold. A lightweight browser-based prototype was implemented with HTML, JavaScript, and the face-api.js library to demonstrate real-time, client-side operation without specialized hardware. Using a simulated single-identity evaluation across 300 login attempts spanning frontal, off-angle, low-light, eyewear, occlusion, and impostor scenarios, the system achieved an accuracy of 97.0%, precision of 97.96%, recall of 96.0%, and an F1-score of 96.97%, with a False Acceptance Rate of 2.0%, a False Rejection Rate of 4.0%, and an average login time of 1.28 seconds. The results indicate that deep-learning face recognition is a practical and accurate alternative to passwords, while highlighting residual challenges related to illumination, pose, and presentation (spoofing) attacks that motivate the integration of liveness detection and multi-factor authentication.

Keywords

Face Recognition Deep Learning Login Security CNN Authentication System

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How to Cite

Sopian, A., Ningtyas, S., & S., U. (2026). Face Recognition-Based Login Security System Using Deep Learning. JINAV: Journal of Information and Visualization, 7(2), 205–216. https://doi.org/10.35877/454RI.jinav4886