Optimasi Pipeline Pengenalan Wajah Berbasis Mobile FaceNet menuju Autentikasi yang Liveness (Liveness-Aware)
TL;DR
This study presents an optimized face recognition pipeline by integrating the MobileFaceNet model in TensorFlow Lite (TFLite) format into a React Native application, reinforced with a liveness detection module to ensure the presence of a genuine face.
Abstract
Bi Biometric face authentication on mobile devices faces two main challenges: computational efficiency under limited resources and vulnerability to spoofing attacks using photos or videos. This study presents an optimized face recognition pipeline by integrating the MobileFaceNet model in TensorFlow Lite (TFLite) format into a React Native application, reinforced with a liveness detection module to ensure the presence of a genuine face. The developed system consists of four main stages: (1) real-time face detection using Expo Face Detector, (2) preprocessing involving cropping and resizing to 112 × 112 pixels with pixel normalization to the range of [−1, 1], (3) MobileFaceNet inference to generate 128-dimensional face embeddings, and (4) verification using cosine similarity with a threshold of 0.75. Initial testing shows that the system operates entirely on-device without relying on external servers, with inference latency suitable for real-time attendance applications. The integration of liveness detection is proposed as an additional layer of defense against presentation attacks. The main contribution of this study is an integrated, lightweight, and secure pipeline architecture that can be implemented on mid-range mobile devices.